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Self-Supervised Representation Learning for Diagnosis of Cardiac Abnormalities on Echocardiograms

Self-Supervised Representation Learning for Diagnosis of Car...

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

作者： Krishnamoorthy, Ramkumar Agrawal, Ajay Agarwal, Puneet Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Mechanical Engineering Uttar Pradesh Mathura India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

Self-supervised representation is trendy in developing new gadget-mastering techniques to enhance diagnostic accuracy for diagnosing modern cardiac abnormalities. In this paper, we speak about the applicability and capacity of present-day self-supervised illustration to gain modern knowledge for analyzing cardiac abnormalities on echocardiograms. We talk about the impact of modern-day supervised and unsupervised gaining knowledge state modern techniques on feature extraction from echocardiogram facts. We also speak about the unsupervised mastering techniques for characteristic extraction, including a self-supervised representation trendy model for directly detecting cutting-edge cardiac abnormalities. The proposed model combines recurrent neural networks with a car-encoder to extract useful excessive-level functions from echocardiogram information and classify the abnormality. We exhibit the accuracy modern our proposed model with the experimental results on two echocardiography datasets. Our proposed version finished promising outcomes and outperformed existing processes. The results imply our proposed version's capacity to enhance the generalization of trendy cardiac abnormality analysis and reduce the education time... © 2024 IEEE.

关键词： diagnosis proposed representation supervised unsupervised

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High-Precision Vehicle Positioning technology by Combining Vehicle Images and Satellite Maps

High-Precision Vehicle Positioning Technology by Combining V...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Chen, Yu-Xuan Lin, Huei-Yung National Chung Cheng University Department of Electrical Engineering 168 University Road Chiayi621 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106 Taiwan

ISBN: (纸本)9781665410205

With the continuous advances of technologies, the demand for precise vehicle positioning has grown significantly. Although it is now possible to capture driving scenes and record driving paths using a dashcam, standard civilian GPS typically has the accuracy errors ranging from 3 to 5 meters. This level of precision is in general not sufficient for the rapidly evolving ADAS (Advanced Driver Assistance Systems). In this paper, we present a high-precision vehicle positioning technique based on satellite map and image data. Instead of using expensive LiDAR sensors, the proposed approach utilizes lane detection, semantic segmentation and geolocation to extract environmental features from images. A∗ algorithm is then adopted to refine the driving trajectory for the improvement of vehicle positioning accuracy. Furthermore, we establish an image dataset containing satellite maps and latitude/longitude coordinate information of various road scenes. The code and datasets are made available publicly at https://***/M6104150181M610415018-Paper. © 2024 IEEE.

关键词： Semantic Segmentation

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Comparative Modeling of CPU Execution Times of AES and ECC-AES Hybrid Algorithms Over Mobile Cloud Environment 10th

Comparative Modeling of CPU Execution Times of AES and ECC-A...

引用

10th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2022

作者： Thomas, Ambili Lakshmi Narasimhan, V. Department of Information Technology Botho University Gaborone Botswana Department of Computer Science Paulson College of Engineering and Computing Statesboro United States

ISBN: (纸本)9789811975233

This paper presents an extended comparative study on the modeling of CPU execution times for AES and a hybrid encryption algorithm AES-ECC. The encryption key is generated using the ECC algorithm, and then, this key is passed to the AES algorithm to encrypt the content of a data file. The ECC key integration with the AES encryption algorithm results in substantial increase in the security of the encrypted data. However, this study shows that the hybrid encryption algorithm utilizes more CPU execution time to encrypt the data file, when compared to the AES algorithm. Applications which demand high security are suggested to use the hybrid algorithm, whereas applications which demand faster execution are suggested to use the AES algorithm. The performance metric used for this study is the CPU execution time and is calculated using the C#.net framework and the assembly code. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile cloud computing

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A Peer-to-Peer Trading Mechanism Considering Preferences: A Two-Stage Game Theory Approach 6

A Peer-to-Peer Trading Mechanism Considering Preferences: A ...

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6th International Conference on Energy Systems and Electrical Power, ICESEP 2024

作者： Wang, Qian Liu, Yitong Xie, Yi Liu, Xing Cheng University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Electronics and Information Technology Sun Yat-sen University Guangzhou China School of Information and Intelligent Engineering Guangzhou Xinhua University Guangzhou China

ISBN: (纸本)9798350353563

This paper presents a peer-to-peer (P2P) energy trading mechanism based on two-stage game theory. It classifies prosumers as buyers and sellers, according to the net energy during the trading period. The interaction between buyers and sellers is modeled as Stackelberg game, and the interaction between buyers is modeled as mixed-strategy game. By solving the models, the optimal strategies for prosumers are obtained. To adapt to the diverse preferences of individual prosumers, a trading mechanism is designed. The simulation results show that the mechanism can not only effectively improve the economic benefits of the system, but also satisfy the individual preferences of the participants, and realize efficient and personalized energy trading. © 2024 IEEE.

关键词： Marketplaces

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The Performance Analysis of Non-Stationary Time Series Analysis for Low Energy Data Aggregation

The Performance Analysis of Non-Stationary Time Series Analy...

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

作者： Agrawal, Ajay Sahal, Nisha Krishnamoorthy, Ramkumar Sanskriti University Department of Mechanical Engineering Uttar Pradesh Mathura India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Non-desk bound Time collection analysis has become increasingly crucial in the current extensive statistics environment because it comprehensively evaluates low-power statistics. It refers to the analysis of temporal records, which reveals modifications in its suggestion, autocorrelation, variance, or different statistical homes over favorable time intervals. Such adjustments might be decided by an expansion of things, including environmental conditions, technological tendencies, and fluctuations in monetary interest. In this research paper, we can study the overall performance of Non-stationary Time series analysis for Low electricity facts Aggregation, focusing on the accuracy and stability of its results. We can compare the accuracy of the conventional methods for the class of non-desk bound time-series statistics, and we will additionally compare answers to reduce electricity consumption. This paper aims to provide meaningful insights into the effectiveness of Non-desk bound Time collection analysis for Low energy facts Aggregation and, consequently, enable its broader adaptation in actual-world programs. The paper offers a performance evaluation of Non-stationary Time collection evaluation (NTSA) for low-strength facts aggregation (LEDA) programs. NTSA is a new method to record Aggregation that considers the non-stationary nature of statistics sets, which usually makes classical aggregations challenging to model correctly. The performance of NTSA is then compared to conventional methods of aggregating low-electricity facts, including Savitzky-Golay filtering (SGF) and transferring average (MA), to understand its skills better. The results advocate that NTSA can produce more correct fashions over a wide range of information units than the conventional techniques, imparting a far better manner to a mixture of low-strength information. Diverse implementations consisting of an optimized set of rules and genetic algorithms are also discussed and are proven to offer

关键词： accuracy autocorrelation performance suggestion temporal

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Domain Adaptation Using Generative Adversarial Networks for Medical Image Synthesis 5th

Domain Adaptation Using Generative Adversarial Networks for ...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Ganesh, D. Verma, Upendra K. Patil, Smita Alam, Intekhab Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India 3School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Latest advancements in state-of-the-art scientific photograph evaluation depend heavily on the availability of large new datasets of cutting-edge annotated scientific photographs for version training. Unfortunately, obtaining massive samples of cutting-edge categorized scientific picas is ultra-modern high-priced, or impossible. Area edition is a way to generalize a version educated on a selected dataset to a brand new area, wherein the model turned into not directly educated. Generative adverse Networks (GANs) were used to generate realistic artificial photographs. It has enabled the opportunity present day the usage of GANs to bridge the domain hole via generating artificial medical photos, which could then be used for education fashions. On this paper;we evaluate the latest progress in making use of area edition the use of GANs for scientific photosynthesis, with an emphasis on applications including pathology, scientific imaging, and optical acceptable imaging. We talk about the benefits of ultra-modern using GANs for area adaptation and present processes for each present day of the above programs. We also speak about various challenges and future directions in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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A Convolutional Neural Network for Goal Recognition 26

A Convolutional Neural Network for Goal Recognition

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26th International Conference on computer and information technology, ICCIT 2023

作者： Tithi, Nusrat Jahan Shatabda, Swakkhar Bangladesh University of Professionals Department of Information and Communication Technology Bangladesh United International University Department of Computer Science and Engineering Bangladesh Bangladesh

ISBN: (纸本)9798350359015

Goal recognition is a complex process that involves deciphering objectives from observable cues, allowing for a deeper understanding of human or machine intentions. It revolves around the fundamental concept of goal inference, focusing not on the specific actions themselves but rather on the underlying purpose or aim behind those actions. Many approaches to goal recognition typically require careful domain design of the agent's potential actions within an environment by a domain expert or involve costly real-time computations, or sometimes both. Recently, alternative goal recognition approaches have emerged with the aim of eliminating the need for meticulous, manual domain design and the reliance on expensive online planners executions. One of these approaches to model-free goal recognition involves machine learning classification tasks, which is the approach we have adopted in our work. The purpose of our work is to present a straightforward Convolutional Neural Network based architecture that achieves better goal-prediction accuracy and timeliness, as demonstrated through rigorous experimental analysis on standard evaluation domains. © 2023 IEEE.

关键词： Automated Planning CNN Goal Recognition Machine Learning

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Phases Distortion Investigation in Beyond 5G mm-Wave OFDM

Phases Distortion Investigation in Beyond 5G mm-Wave OFDM

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kale, Kiran Raja Praveen, K.N. Savita Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

Fifth-generation wireless networks are ready to handle the increasing volume of mobile traffic and provide critical technological solutions to suit various service needs. Utilizing the millimeter-wave (mm-wave) spectrum is a crucial strategy for achieving large data speeds. Additionally, analogue radio-over-fiber (ARoF) has a wide range of benefits, such as affordability, little power usage, and exceptional spectrum efficiency. As a result, mm-wave ARoF becomes a strong candidate for providing becomes the standard waveform for 5G, it is crucial that mm-wave ARoF systems support it. However, in mm-wave OFDM ARoF systems, an existence of phase interference represents a substantial performance-limiting constraint. Through a painstakingly constructed test setup, we perform a thorough examination of phase noise in this study. We are able to systematically modify the system's overall last phase noise level thanks to this configuration. In addition, we provide a fresh and unique approach for OFDM transmitter phase noise compensation. Our testing confirms the algorithm's effectiveness under these circumstances and highlights the potential advantages of mm-wave OFDM ARoF for 5G to meet by evaluating how well it works over a range of period noise values and subcarrier spacings. © 2023 IEEE.

关键词： Analogue Radio-over-Fiber Beyond 5G Distortion Frequency-Selective Channels Investigation Millimeter-Wave MM-Wave OFDM Phases Spectral Efficiency and Signal-to-Noise Ratio Subcarrier Spacings

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Evolving Mobile Cloud: Enhancement Technologies and Future Approaches

Evolving Mobile Cloud: Enhancement Technologies and Future A...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Mahesh, T.R. Kumar, Ritesh Kehkeshan Jallal, S. Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

There are still limitations in terms of life of the battery and processing power that make it difficult to fulfill the expanding needs of mobile users, despite the fast progress in hardware capacity or the widespread usage of mobile devices. Both conventional and cutting-edge approaches have been investigated to close the distance between customer demands and the constraints of mobile devices. In the context of the mobile cloud, current studies have concentrated on boosting the performance for mobile devices using augmentation approaches. Our emphasis is on the methods themselves, as opposed to current taxonomies in this area, with the aim of establishing a whole mobile computing cloud system. With the goal of ensuring dependable mobile cloud services, this survey may be used as a reference for implementing the augmentation approaches that are now accessible in portable cloud computing systems. We also talk about the unresolved issues and prospective directions for further study in this area. © 2023 IEEE.

关键词： augmentation approaches context sensitivity data interoperability Mobile cloud mobile storage augmentation parallel execution privacy and security and incentive mechanisms reliability resource amplification

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Fostering Smart Agriculture: Using Vision-Based AI for Livestock Managing

Fostering Smart Agriculture: Using Vision-Based AI for Lives...

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2023 International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2023

作者： Shukla, Gaurav Gowri, K. Sudhamsu, Gadug Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Jain University Faculty of Engineering and Technology Department of Computer Science Engineering India

ISBN: (纸本)9798350344387

The swine industry constitutes a significant segment within livestock farming. However, conventional methods employed for monitoring pig health and welfare are laborious and time-intensive. Relying on these traditional monitoring techniques becomes increasingly taxing for both pigs and farmers, particularly in the face of the expanding scale of hog farming. This stress, in turn, impacts pig welfare and overall economic productivity. Integrating artificial intelligence (AI) has emerged as a fundamental component in the realm of intelligent pig farming. Advanced pig farming systems leverage AI integration to continuously monitor biometric data encompassing pig vocalizations and behavior. This data, acquired through sensors such as cameras and RF ID devices, is transformed into pivotal indicators of pig health and welfare. Analyzing these essential variables enables the early identification of health issues affecting pigs, facilitating prompt interventions and treatments that significantly enhance production efficiency and financial outcomes in pig farming. This paper presents a comprehensive overview, highlighting the challenges and opportunities within precision pig husbandry. The fusion of AI technologies with traditional farming practices presents novel prospects and hurdles, shaping the landscape of modern pig farming. Through this evaluation, the paper aims to underscore the transformative potential and the evolving landscape of precision-based pig farming. © 2023 IEEE.

关键词： Artificial intelligence (AI) Livestock management Pig farming Pig health Precision farming and Behavioral analysis Sensors Smart agriculture Vision-based AI Welfare monitoring

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