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Zero-Shot Low-Light-Level Image Enhancement Algorithm Based on Atmospheric Scattering Model 5

Zero-Shot Low-Light-Level Image Enhancement Algorithm Based ...

引用

5th international conference on Machine Learning and computer Application, ICMLCA 2024

作者： Shi, Shijie Zhou, Dake Shao, Yiming College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798331530334

Low-light image enhancement remains a pivotal area of focus within the realm of image processing. While current learning-based enhancement techniques have shown advancements in managing noise or color distortion, they often fail to strike an optimal balance between these two aspects and are constrained by the availability and quality of training data. In this research, we introduce a novel zero-shot learning method for low-light image enhancement, named ZRASM, which leverages the Atmospheric Scattering Model (ASM) and integrates a denoising network grounded in the Noise2Noise theory. ZRASM is capable of noise reduction and color restoration without the need for external training data, operating solely from a single input image. Our experimental results indicate that ZRASM surpasses several state-of-the-art models in terms of image enhancement performance. © 2024 ieee.

关键词： Zero-shot learning

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Bio-signal Analytics and Machine Learning for Medical Imaging Techniques 1

Bio-signal Analytics and Machine Learning for Medical Imagin...

引用

1st international conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Sood, Danish Bharat, Lakshmi Jha, Ankita Kanhere, Jay Rani, D Shobha Sinha, Ranjana Department of Radiodiagnosis Karad India Gi India Automation & Systems Pvt. Ltd Pune India Institute of Aeronautical Engineering Department of Electrical and Electronics Engineering Hyderabad India Dr. Shyama Prasad Mukherjee University Department of Botany Ranchi India

ISBN: (纸本)9798350360707

Modern engineering science and the area of medicine, which are traditionally seen as two seemingly opposed poles of academic brilliance, are now in close contact thanks to the discipline of biomedical engineering. Currently, the field of image and signal processing is expanding quickly in both the academic and industrial domains. These days, the healthcare and engineering disciplines heavily rely on the processing, analysis, and diagnosis of bio-signals, or pictures. Due to the rapid expansion of information technology, students have access to a wide range of tools that allow them to study any area of science or engineering. For this three specific human physiological bio-signals: breathing rate (BR), heart rate (HR), and blood oxygen saturation level (SpO2) are monitored for the analysis. © 2024 ieee.

关键词： Signal analysis

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GraphUC: Predictive Probability-Based Graph-Structured Data Augmentation Model for Class Imbalance Node Classification 4

GraphUC: Predictive Probability-Based Graph-Structured Data ...

引用

4th international conference on Electronic Information engineering and computer science, EIECS 2024

作者： Shi, Meilun Zuo, Enguang Qu, Hanwen Lv, Xiaoyi School of Computer Science and Technology Xinjiang University Xinjiang Urumqi China College of Intelligent Science and Technology Xinjiang University Xinjiang Urumqi China School of Software Xinjiang University Xinjiang Urumqi China

ISBN: (纸本)9798331531409

Class imbalance in graph neural networks (GNNs) poses a significant challenge for node classification tasks, often resulting in poor performance on minority classes due to the over-representation of majority class nodes. To address this, we introduce GraphUC, a novel graph data enhancement method that leverages predictive probabilities to identify and generate hard-to-classify minority class nodes. Meanwhile, designing N-SHA synthesises new nodes by integrating neighborhood information, effectively expanding the potential space for minority classes. Experimental results on real-world datasets such as Cora and CiteSeer demonstrate that GraphUC substantially enhances GNN's classification accuracy for minority class nodes, with a maximum improvement of 30.24% in GCN performance, while maintaining accuracy for majority class nodes. This significant advancement underscores the effectiveness of GraphUC in tackling class imbalance issues in node classification. © 2024 ieee.

关键词： class imbalance data augmentation node classification

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An Evaluation System for Large Language Models based on Open-Ended Questions 11

An Evaluation System for Large Language Models based on Open...

引用

11th ieee international conference on Cyber Security and Cloud Computing, CSCloud 2024

作者： Cao, Zhiyuan Ma, Zeyu Chen, Mingang Computer and Information Engineering College Shanghai Polytechnic University Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Mathematical and Science College Shanghai Normal University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Shanghai China

ISBN: (纸本)9798350376982

We designed a large language model evaluation system based on open-ended questions. The system accomplished multidimensional evaluation of LLMs using open-ended questions, and it presented evaluation results with evaluation reports. Currently, the evaluation of large-scale language models often exists with two prominent limitations: (1) The evaluation methods are often single-minded, resulting in less credible results. (2) Most evaluations are based on datasets with closed-ended questions, treating generative large language models as discriminative models, which fails to adequately reflect the high output flexibility characteristic of these models. For these two limitations, we proposed an evaluation system for LLMs based on open-ended questions. Our experiments on the adapted open-source datasets demonstrated the effectiveness of this system. © 2024 ieee.

关键词： Natural language processing systems

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Novel Data-Driven Dynamic Network science Application in Algorithmic Trading 48

Novel Data-Driven Dynamic Network Science Application in Alg...

引用

48th Annual ieee international computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Ranathungage, Thimani Thavaneswaran, Aerambamoorthy Liang, You Thulasiram, Ruppa Paseka, Alex Univ Manitoba Dept Stat Winnipeg MB Canada Toronto Metropolitan Univ Dept Math Toronto ON Canada Univ Manitoba Dept Comp Sci Winnipeg MB Canada Univ Manitoba Dept Accounting & Finance Winnipeg MB Canada

ISBN: (纸本)9798350376975;9798350376968

One of the recent developments in computational finance has been the rise in using graph-based approaches to analyse stock market dynamics systematically. In algo trading literature, stocks for pairs trading and multiple trading are commonly selected by using Engle Granger and Johansen co-integration tests. Identifying all pairs eligible for trading in large datasets, entails a significant computational burden. In this paper, price correlation-based dynamic networks and differenced price series-based correlation networks and their importance ranks are used to pre-select pairs for trading. Algorithmic trading profits using commonly used co-integration methods are compared with the profits made by proposed correlation-based financial networks. Unlike the existing work, the novelty of the paper is that it uses data-driven correlation-based financial network approach to propose a stock selection method for pairs trading. In this paper, profit per transaction is used to compare the trading strategies. The superiority of the financial network stock selection method is discussed as well in some detail.

关键词： Dynamic Trading Strategies Financial Networks PageRank Algorithm Co-integration

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Artificial Intelligence Applications in Accounting and Finance

Artificial Intelligence Applications in Accounting and Finan...

引用

2024 ASU international conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Rahim, Rahila Chishti, Mohammad Ahsan National Institute of Technology Department of Computer Science Engineering Srinagar India

ISBN: (纸本)9798350372229

The world of Industry 4.0 embraces the transformation that calls for strategic investments, cooperative partnerships, and a proactive approach to overcome obstacles to successfully transition to smarter, more connected, and more efficient in- dustrial ecosystems. The accounting and finance industry has benefited greatly from process modernization, which has resulted in a significant decrease in the use of the traditional system through the integration of digital technologies. The use of various artificial intelligence technologies such as expert systems for audit and taxation, intelligent agents for customer service, Natural Language Processing (NLP), Robotic Process automation (RPA), Blockchain, data analytics, and machine learning for decision- making has been proposed to automate, streamline, and improve the efficiency of various tasks and processes in accounting and finance operations. The study specifically investigates how the incorporation of AI technologies affects the reliability & timeliness of financial & accounting processes. The paper also presents some use cases and AI applications relevant to this field. © 2024 ieee.

关键词： accounting AI finance ML NLP

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Saline Level Monitoring System

Saline Level Monitoring System

引用

2025 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Supraja, A. Harshith, P. Dugra Devi Sreekar Reddy, B. Mohan, J. Institute of Aeronautical Engineering Dept.of Electronics and Communication Hyderabad India

ISBN: (纸本)9798331529833

The most important and primary commandment for hospitalized patients is to be carefully examined and treated, with adequate nutrition/medicine provided at the appropriate time. Aside from the different therapies they receive from doctors, saline therapy is the most fundamental treatment for dehydration, which improves their condition. However, the patient must be continuously always supervised by the nurse, which is not always practicable due to the large number of beds and other responsibilities. Unfortunately, the patient's blood begins to flow back into the saline tube due to unforeseen circumstances and carelessness. In many situations, the outcome is fatal or life-threatening. To protect their lives from essential consequences and unconditional flows, a smart saline bottle level monitoring and alert system must be designed. The proposed system offers an appropriate approach for sensing saline flow rate and monitoring it utilizing an IoT platform. © 2025 ieee.

关键词： Healthcare Management IoT Real-Time Monitoring Resource optimization Saline monitoring

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Cutting-Edge Predictive Methods for Enhancing Short-Term Load Prediction

Cutting-Edge Predictive Methods for Enhancing Short-Term Loa...

引用

2024 international conference on Electrical and computer engineering Researches, ICECER 2024

作者： Ghazal, Taher M. Janjua, Jamshaid Iqbal Abushiba, Walid Ahmad, Munir Abbas, Tahir Ateeq, Karamath College of Arts & Science Applied Science University P.O.Box 5055 Manama Bahrain Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Selangor Malaysia Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul02841 Korea Republic of TIMES Institute Department of Computer Science Multan Pakistan School of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798331539733

Efficient management and cost-related factors in the power sector call for accurate short-term load forecasting as it enables better planning with the electric grid and achieving stability within it. This paper looks into the state-of-the-art forecasting strategies that enhance load forecasts made in shorter time frames. Such techniques include but are not limited to machine learning applications, effective data models, and multi-dimensional modeling leveraging historical, meteorological, and economic data. Their review is carried out in detail with their advantages and disadvantages and their application in practice. Furthermore, this study strives to understand the barriers that need to be overcome in the quest to increase the accuracy of the forecasting models especially concerning renewable energy sources. We illustrate how new trends relate to future developments of predictive models about supporting the decision-making in operating power systems and improving the reliability and sustainability of energy systems. © 2024 ieee.

关键词： Prediction models

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Differentiate Music Genre from an Audio File Using CNN

Differentiate Music Genre from an Audio File Using CNN

引用

2023 international conference on System, Computation, automation and Networking, ICSCAN 2023

作者： Raja Rajeswari, S. Dhanalakshmi, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Ctr. for Cyber Phys. Syst. Sch. of Comp. Science and Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9798350315127

The complexity of music production has gradually decreased recently, causing a big number of individuals to produce music as a result and submitting it to streaming media. Because there are so many music streaming services, people are putting in a lot of effort looking for certain songs. The result says, it's critical to be able to swiftly classify musical genres in today's culture. As deep learning and machine learning technologies progress, Convolutional Neural Networks (CNN) are indeed being applied in a number of disciplines, and new CNN-based versions have appeared one after the other. Manually extracting features from time-series data is required for traditional music genre classification. Applying deep learning to time-series data has proven to be useful and efficient. To save time for the user when looking for different styles of genres. Librosa is used to convert into a corresponding Mel spectrum. Converted Mel send to a CNN model for training. © 2023 ieee.

关键词： Convolutional neural networks

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Enhancing Energy Efficiency in IoT Networks with AI optimized LEACH Protocol and Digital Twin Simulation 1

Enhancing Energy Efficiency in IoT Networks with AI optimize...

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1st international conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Harinaiha, Adarsha Chaudhary, Pavan Faculty of Engineering and Technology Department of Mechanical Engineering Ramnagar District Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331518578

The convergence of the Internet of Things (IoT) via artificial intelligence (AI) will undoubtedly revolutionize networking. IoT is a technological paradigm shift facilitating the incorporation of many applications, including smart homes and wearable gadgets, via automation, wireless sensors, and embedded systems. Furthermore, the production of AI enhances this phenomenon by delivering data-driven insights, streamlining procedures, and creating new avenues for development. The extensive implementation of devices in Internet of Things (IoT) networks has raised worries over escalating energy usage. Utilizing environmentally friendly techniques is crucial to maintain network service for nodes that sense with limited power supplies. One approach that substantiates this assertion is LEACH, an acronym for Low Energy Adaptive Clustering Hierarchy. The objective of this form of communication is to optimize the transfer of information to base sites by successfully configuring clusters within networks and executing real-time adjustments across cluster nodes. This work enhances the effectiveness of IoT systems by using a virtual twin simulation to optimize the protocol known as LEACH. Employ artificial intelligence analysis and virtual network models to evaluate power consumption and efficiency. Cache nodes are essential in this architecture since they receive information from the cluster's head & transmit it through the main hub. Utilizing simulation tools and artificial intelligence, we can enhance the reliability and energy efficiency of IoT devices. The results indicate an 84% decrease in inactive nodes & a 1.67-fold rise in node energy use relative to traditional methods. The present study suggests a prospective future trajectory for LEACH-based, AI-augmented, low-power IoT infrastructure. © 2025 ieee.

关键词： Energy utilization

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