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Chart Career Using AI and Data Analytics 1

Chart Career Using AI and Data Analytics

引用

1st International Conference on Computational Intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Yogitha Ramani, Roja Nayak, Vaishnavi Sirisha, M. New Horizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350365405

In the realm of educational research, a comprehensive analysis of student performance unfolds through the application of diverse machine learning algorithms. These algorithms serve as advanced analytical tools, distilling raw data into nuanced skills analysis and showcasing the symbiosis of artificial intelligence and educational insights. Beyond algorithmic processes, the efficacy of this journey lies in the subsequent visualization of analyzed data using visualization tools. These visuals elucidate complex patterns, providing us with a clear understanding of academic and cognitive landscapes. This serves as a conduit for machine learning algorithms, to interpret academic destinies and guides the trajectory towards valuable insights for career guidance. © 2024 IEEE.

关键词： Adversarial machine learning

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Profit Demand Delegated Proof of Stake: Hypothetical Design Concept for Blockchain

Profit Demand Delegated Proof of Stake: Hypothetical Design ...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Gupta, Udayan Das Haque, Md. Mokammel Reza, Ahmed Wasif Arefin, Mohammad Shamsul Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong Bangladesh Sciences University of Adelaide School of Computer and Mathematical Adelaide Australia East West University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Delegated Proof of Stake (DPoS) is indeed a fascinating consensus algorithm used on various blockchain platforms. It was designed to address some of the scalability and energy consumption issues associated with Proof of Work (PoW) while maintaining decentralization to a certain extent. But, DPoS has some disadvantages in the distribution of profit share for stake forwarding nodes to delegate nodes. The profit share is controlled by the delegate nodes rather than the staking nodes or token holder voting nodes. Here, in Profit Demand Delegated Proof of Stake (PD-DPoS), we introduced a new mechanism to facilitate the token holder voting nodes to demand a rate of profit/profit share. PD-DPoS will encourage the delegate nodes to invest in infrastructure for high connectivity and fast computing crypto systems. As the system builds targeting fast and reliable computing by extending the opportunity of demanding profit share for token holder voter nodes will experimentally perform and prove better than the DPoS. © 2024 IEEE.

关键词： Blockchain

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Manufacturing High-Q Silicon Nitride Photonic Chips via Silicon Hardmask Etching

Manufacturing High-Q Silicon Nitride Photonic Chips via Sili...

引用

2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Liu, Shuai Zhang, Yuheng Zhang, Zheshen Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

ISBN: (纸本)9781957171395

We present a robust process for fabricating high-Q, dispersion-engineered Si3N4 photonic chips using amorphous silicon hardmask etching with PECVD SiO2 cladding, achieving an intrinsic quality factor up to ∼ 17.5×106 and an average intrinsic quality factor of ∼ 13×106. We anticipate a further reduction in propagation loss through LPCVD SiO2 cladding. © Optica Publishing Group 2024, © 2023 The Author(s)

关键词： Silicon nitride

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Fair-Over-Time Distributed Energy Resource Coordination 60

Fair-Over-Time Distributed Energy Resource Coordination

引用

60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Moring, Hannah Farrell, Xavier Mathieu, Johanna L. University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI48109 United States

ISBN: (纸本)9798331541033

There are numerous strategies to coordinate distributed energy resources (DERs) to provide a variety of services to the power grid. DER coordination can affect resources and participants unequally, for example, by excessively degrading or curtailing particular DERs more than others. However, few DER coordination strategies explicitly take into account fairness, equity, or justice. In this paper, we explore fairness metrics and their applicability to the DER coordination problem. In particular, we investigate metrics from machine learning to identify metrics that could be incorporated into DER coordination problems and we summarize fairness metrics that have been used in the power systems literature. A key challenge is that most existing fairness metrics are static - ensuring fairness at a point in time. DER coordination problems are inherently dynamic and we often care about fairness over time, not at each time. The machine learning literature offers some ways to think about fairness over time and, more generally, how to incorporate fairness into dynamic power systems problems. We use a specific DER coordination problem - the problem of computing dynamic operating envelopes - to demonstrate how incorporation of a fair-over-time metric changes DER coordination solutions, and highlight the trade-offs that arise. © 2024 IEEE.

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An Extractive and Abstractive Approach of Text Summarization

An Extractive and Abstractive Approach of Text Summarization

引用

International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023

作者： Kalyana Sundaram, M. Bhuvanesh, R. Hari Narayanan, R. Kalaiselvi, S. Thamarai Selvi, D. Gomathi, V. Department of Computer Science Engineering National Engineering College Kovilpatti628503 India

ISBN: (纸本)9789819746491

Text summarization, a difficult problem in natural language processing, tries to reduce lengthy materials into summaries that are clear and instructive. The "Extract-then-Abstract" approach for unsupervised abstractive summarization is a novel method introduced in this paper. This approach aims to provide more cohesive and human-like summaries as opposed to previous methods, which have trouble with seamless transitions between phrases. The method was designed to mimic how people generate summaries by first isolating the key lines, then fluently reassembling them. The proposed approach consists of two stages. In the extraction stage, TextRank, an unsupervised graph-based algorithm for extractive summarization, is utilized to generate a summary by selecting important sentences from the document. In order to provide a structured and fluid abstractive summary, a BART-based sentence-writing paradigm is introduced. When compared to unsupervised extractive summary techniques, the Extract-then-Abstract method produces summaries with greater coherence and resemblance to human comprehension while still maintaining competitive ROUGE scores. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive Learning

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Identification of Paddy Disease Using Image Processing 2

Identification of Paddy Disease Using Image Processing

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Soorji Martina, K. Shobana, M. Sneha, S. Shaini, Vellanki Sreemathy, J. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350327533

Rice is cultivated almost allover the world. In spite of the usage of several pesticides, a variety of diseases and pests reduce rice yield. Pesticide use reduces crop quality while increasing crop production. Thus, one of the key project in the field of computer science and agriculture department is the identification of diseases from captures of rice. This research study uses CNN to describe and classify various rice plant diseases. Processing techniques for images such as changing the size., rescaling, restoring equilibrium, and transforming into colors are carried out for recovering vital disease-related features. The approaches are highly helpful for farmers to quickly take the necessary corrective actions to protect their crops from diseases. In order to examine the current situation regarding the agricultural decision support system, this study has explored a number of research articles on the identification of rice plant diseases. This research study analyzes the datasets to obtain the required deep learning framework. The proposed model differentiates healthy leaves and disease-affected leaves. © 2024 IEEE.

关键词： CNN Deep Learning Dense Net Algorithm Image Processing

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Learnscan: An Interactive Learning Platform for Enhanced Education Using Augmented Reality 4

Learnscan: An Interactive Learning Platform for Enhanced Edu...

引用

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Kamath, Ashwini Deepikaa, T.R. Godlin Donya, S.P. Pavithra, R. Madhumathi, R. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331529635

An Interactive Learning Platform for Enhanced Education using Augumented Reality (AR) presents the development of an innovative educational website and app designed to enhance learning experiences through the integration of QR codes and Augmented Reality technology. The platform features a curated list of QR codes, each assigned to specific educational topics. Upon scanning a QR code, users are directed to a dedicated website showcasing a detailed 3D model related to the QR code's assigned subject matter. Scanning a QR code associated with 'heart' will lead to a 3D model of the human heart. The website offers an interactive interface that allows users to explore and manipulate the 3D models in real-time. Also, the platform incorporates AR functionality, enabling users to view and interact with the models in their physical environment. It is also aided with audio recordings and description related to the information which is provided in the website. This immersive approach aims to provide a more engaging and effective learning experience, especially for complex subjects where visualizations are beneficial. Key features of the platform include user-friendly navigation, highquality 3D models, and seamless integration with AR technology. © 2024 IEEE.

关键词： 3D modeling

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CLASS PROBABILITY MATCHING WITH CALIBRATED NETWORKS FOR LABEL SHIFT ADAPTATION 12

CLASS PROBABILITY MATCHING WITH CALIBRATED NETWORKS FOR LABE...

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Wen, Hongwei Betken, Annika Hang, Hanyuan Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

We consider the domain adaptation problem in the context of label shift, where the label distributions between source and target domain differ, but the conditional distributions of features given the label are the same. To solve the label shift adaptation problem, we develop a novel matching framework named class probability matching (CPM). It is inspired by a new understanding of the source domain's class probability, as well as a specific relationship between class probability ratios and feature probability ratios between the source and target domains. CPM is able to maintain the same theoretical guarantees as the existing feature probability matching framework, while significantly improving the computational efficiency due to directly matching the probabilities of the label variable. Within the CPM framework, we propose an algorithm named class probability matching with calibrated networks (CPMCN) for target domain classification. From the theoretical perspective, we establish a generalization bound of the CPMCN method in order to explain the benefits of introducing calibrated networks. From the experimental perspective, real data comparisons show that CPMCN outperforms existing matching-based and EM-based algorithms. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Computational efficiency

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Crop Yield Prediction using Machine Learning 15

Crop Yield Prediction using Machine Learning

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Tony, Akash Lalkumar, Dona Saint, Luke Mohammed Hazim, A.S. Department of Computer Science and Engineering Sahrdaya College of Engineering and Technology Thrissur India

ISBN: (纸本)9798331300579

As the world faces growing challenges in ensuring food security, predicting crop yields accurately be- comes increasingly crucial. Traditionally, this prediction re- lied on methods limited by their inability to handle complex interactions between various factors like climate, soil, and farming practices. This is where Machine Learning (ML) emerges as a game-changer. By harnessing the power of data-driven insights, ML offers highly accurate predictions surpassing traditional methods. This paper specifically dives into the potential and challenges of utilizing ML for crop yield prediction, highlighting its strengths and areas for further development. While traditional approaches often fall short in capturing the intricate relationships impacting yield, the proposed method leverages satellite imagery to extract valuable features and deliver precise predictions on granular levels. Ultimately, this paves the way for farmers to make informed decisions, optimize resource allocation, maximize production, and contribute significantly to global food security. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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A Hybrid Approach for Denoising and Recognition of Handwritten Characters Using Deblur GAN-CNN 4

A Hybrid Approach for Denoising and Recognition of Handwritt...

引用

4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Bharati, P. Vijaya Vignan's Institute of Engineering for Women Department of Computer Science and Engineering India

ISBN: (纸本)9798331518981

Handwritten Character Recognition (HCR) is a critical area of research within pattern recognition and artificial intelligence, with applications spanning from document digitization, optical character recognition (OCR), and handwriting analysis. Despite significant technological advancements, accurate recognition of handwritten characters remains challenging due to variations in writing styles, sizes, and inherent *** paper presents a novel approach to HCR, utilizing a combination of Deblur Generative Adversarial Networks (GAN) and Convolutional Neural Networks (CNN) to effectively address these challenges. Unlike traditional deblurring methods, which often introduce artifacts or blur the edges of characters, Deblur GAN is specifically trained to enhance the clarity of handwritten images while preserving essential character features. This allows the subsequent CNN to extract more accurate and discriminative features, leading to improved character *** extensive experimentation on benchmark datasets such as MNIST, an approach that significantly outperforms the existing methods is demonstrated in terms of recognition accuracy, particularly in scenarios involving blurred or indistinct handwriting. These findings highlight the potential of integrating deblurring techniques with deep learning models to address persistent challenges in handwritten character recognition, paving the way for enhanced automation and efficiency in text analysis tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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