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Interpretable Cervical Cell Classification: A Comparative Analysis 4

Interpretable Cervical Cell Classification: A Comparative An...

引用

4th international conference on advanced Research in computing, ICARC 2024

作者： Gnanavel, Nishaanthini Inparaj, Prathushan Sritharan, Niruthikka Meedeniya, Dulani Yogarajah, Pratheepan University of Moratuwa Department of Computer Science and Engineering Moratuwa Sri Lanka School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom

ISBN: (纸本)9798350384864

Cervical cancer is a significant global health issue, and traditional screening methods like Pap smears are labor-intensive and may miss some cases. Automation is needed, but it faces challenges in terms of interpretability and data availability. To address this, the paper proposes using Explainable Artificial Intelligence (XAI) techniques like GradCAM, GradCAM++, and LRP to improve the transparency and interpretability of a cervical cell classification model, making it a novel contribution to enhancing the trustworthiness of automated cervical cancer detection. Using the Herlev dataset, we employ data pre-processing, data augmentation techniques and develop a binary classification model, achieving a 91.94% accuracy with VGG16. The qualitative analysis of XAI methods confirmed that the model relied on nucleus and cytoplasm features, key indicators of malignancy. The least mean image entropy of 2.4849 and steep prediction confidence drop with perturbations quantitatively proved Layer-wise Relevance Propagation (LRP) to be the most effective XAI technique for cervical cell classification. © 2024 ieee.

关键词： Artificial intelligence

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Graph data Science Application in Omni-Channel

Graph Data Science Application in Omni-Channel

引用

2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Vallurupalli, Naina Chowdhary Vijayakumar, K. Walmart Inc United States St. Josephs's Institute of Technology Department of Information Technology OMR Chennai India

ISBN: (纸本)9798331543617

In today's competitive retail environment, omni-channel e-commerce has become a critical strategy for delivering seamless customer experiences across diverse touchpoints such as websites, mobile apps, physical stores, and social media platforms. The complex relationships and interactions between customers, products, and channels present significant analytical challenges. Graph data Science (GDS) offers a powerful framework to model and analyze these relationships, providing actionable insights to optimize customer engagement, detect fraud, and streamline supply chains. By representing entities such as customers and products as nodes, and their interactions as edges, GDS enables the use of advanced algorithms for community detection, link prediction, and anomaly detection. This paper explores the use of graph neural networks (GNNs) for tasks such as customer segmentation, personalized recommendations, and cross-channel journey optimization. Through real-world applications, the paper emphasizes how GDS transforms large-scale, unstructured e-commerce data into actionable insights, driving business growth and enhancing operational efficiency. This comprehensive analysis underscores the potential of GDS as an essential tool for retailers navigating the complexities of omni-channel commerce. © 2024 ieee.

关键词： Sales

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Efficiency Electrical Power Edge-end computing with Patch-based Low-rank Transformer

Efficiency Electrical Power Edge-end Computing with Patch-ba...

引用

2024 international conference on Energy and Electrical Engineering, EEE 2024

作者： Zhou, Mi Qian, Bin Lin, Xiaoming Zhang, Fan Luo, Yi Wu, Binquan Electric Power Research Institute Csg Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid Guangzhou China

ISBN: (纸本)9798350341768

The development of communication technology has made the need to build more reliable and efficient smart power grid systems imminent. The emergence of edge computing has significantly alleviated the pressure of data transmission in systems, enhancing the reliability and stability of cloud-edge based smart grid systems. However, the deep algorithms on which current Edge computing platforms rely increase the overall computation time consumption and memory consumption of the whole system, which is inconsistent with the responsiveness and reliability requirements of smart power grid system services. Therefore, in this paper, we propose Patch-based Low-rank Transformer (PLT) that balances accuracy and efficiency, which can greatly alleviate the computation time consumption and memory consumption of Edge computing platforms. Specifically, we cut the original electric power time series into subsequence-level patches to ensure the accuracy of the model for long and short-term sequential dependencies learning. Then, we introduce a low-rank attention mechanism to reduce the computational consumption of the model, and design a simple but efficient knowledge distillation module that transferring knowledge from the Transformer-based model to a lightweight MLP to speed up the computation of the model at inference phrase. Extensive experiments on 11 public datasets (10 UCR and 1 cluster-trace-v2018 released by Alibaba Cloud) and 13 collected electric power time series datasets show that the proposed PLT outperforms baseline models and reaches the design purpose. © 2024 ieee.

关键词： Power systems computer aided design

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Utilization Of data Analytics in Analysing Audio and Crowd Video Content 9

Utilization Of Data Analytics in Analysing Audio and Crowd V...

引用

9th international conference on advanced computing and Communication systems, ICACCS 2023

作者： Singh, Anshy Kumar, Manoj Saxena, Aditi GLA University Dept. of Computer Engineering and Applications Mathura India GLA University Dept. of Electronics and Communication Engineering Mathura India

ISBN: (纸本)9798350397376

The development of growing variety of useful sources of data, the creation of a number of methods for machine learning, and the development of web of things and advanced analytics enable people and policy maker with new tools for predictive services however an interconnected system also has a deficit that will help in reducing building costs and allow new forms of facilities this is a case in point for an applied big data analysis framework of things and smart grid for web applications, this paper presents a summary and description of big data and the internet of the creation of *** paper provides a comparative analysis of audio and crowd video content an intelligent grid case study that shows the high standards of big data analysis *** evaluation processing, analysis and reporting for comprehension and optimization gather and analyzes, of learner data in their contexts and in the condition in which it is generated. © 2023 ieee.

关键词： Big data

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Machine Learning Detection Approach Against Trust Manipulation Attack in Trust Management systems 6

Machine Learning Detection Approach Against Trust Manipulati...

引用

6th international conference on intelligent computing in data Sciences, ICDS 2024

作者： Baalla, Mohcine Driss, Bouzidi National School of Computer Science and Systems Analysis ENSIAS Mohammed v University in Rabat Rabat Morocco

ISBN: (纸本)9798350351200

Trust Management systems (TMS) play a crucial role in establishing and maintaining trust among nodes in various contexts. However, some nodes may adopt suspicious behaviors by strategically alternating between trustworthy and malicious actions to evade detection, an attack called Trust Manipulation Attack (TMA) or on-off attack. This paper addresses the challenge of detecting such suspect behaviors in TMS and proposes a machine learning-based approach for TMA identification. To validate our contribution, we utilize a Vehicular Ad Hoc Network (VANET) dataset generated through a series of simulations. The results obtained from our experiments demonstrate the effectiveness of the proposed approach in accurately identifying nodes with alternating trust patterns, thus effectively detecting suspect behaviors within the TMS. In a machine learning model, the accuracy reached up to 97.78%. © 2024 ieee.

关键词： Adversarial machine learning

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A Review of Research on data Empowerment 9

A Review of Research on Data Empowerment

引用

9th international conference on intelligent Informatics and BioMedical Sciences, ICIIBMS 2024

作者： Xia, Xu Shanghai Institute of Technology School of Economics and Management Shanghai China

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363043

With the development of cloud computing, Internet of Things, big data and other digital technologies, more and more scholars have begun to pay attention to the research related to data empowerment, but the existing research has problems such as confusing concepts and confusing systems. This paper uses the literature research method to clarify the conceptual basis of data Empowerment, including the development history of data Empowerment, the definition, form and main object of data Empowerment, the definition and differentiation of related concepts, and data Empowerment and related theoretical research, and finally, in view of the current status of the research, it looks forward to the future direction of the data Empowerment research in terms of the basic research, the research methodology and the model. © 2024 ieee.

关键词： data centers

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Energy Prediction for Automobile Air Conditioning systems

Energy Prediction for Automobile Air Conditioning Systems

引用

2024 ieee international conference on Big data, Bigdata 2024

作者： Tazhibi, Adel Dong, Nathan Kothari, Kavyalata Wang, Taehyung University of California Computer Science Department Los AngelesCA United States Georgia Institute of Technology College of Computing New York United States California State University Department of Computer Science CA United States

ISBN: (纸本)9798350362480

Significant energy wastage and increased CO2 emissions from automobile air conditioning systems highlight the critical need for enhanced energy prediction and management. This study investigates the potential of machine learning algorithms for energy prediction in A/C systems, focusing on the Toyota Prius Hybrid model. By integrating vehicle data with external weather information, we developed a predictive model that accurately forecasts energy usage and identifies key factors influencing efficiency. These findings could facilitate the creation of intelligent control systems for automotive air conditioning, leading to enhanced energy savings and reduced emissions. © 2024 ieee.

关键词： intelligent systems

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Enhancing Auditing Quality through Big data Analytics: A Study Leveraging the Technology Acceptance Model in computing and Processing 6

Enhancing Auditing Quality through Big Data Analytics: A Stu...

引用

6th international conference on Cybernetics and intelligent System, ICORIS 2024

作者： Harijanto, Nicholas Pradana Veronica Riantono, Ignatius Edward School of Information Systems BINUS University Jakarta Indonesia School of Accounting BINUS University Jakarta Indonesia

ISBN: (纸本)9798331539603

In the rapidly evolving landscape of global technology, information technology (IT) and electronic resources have become crucial assets for businesses. This study investigates the utilization and impact of Big data technology in the audit processes of public accounting firms in Indonesia, examining how these technologies enhance audit quality, efficiency, and risk assessment. Utilizing the Technology Acceptance Model (TAM), the research explores auditors' perceptions of the usefulness and ease of use of Big data analytics and their acceptance and application within the audit domain. The findings indicate a positive relationship between perceived contribution, perceived ease of use, and perceived usefulness of Big data Analytics. Moreover, the actual use of Big data Analytics significantly improves audit quality by allowing for comprehensive data analysis and more accurate identification of fraud and errors. This research underscores the critical role of Big data Analytics in Audit Quality, offering a baseline for future studies on the integration of advanced technologies in auditing. © 2024 ieee.

关键词： data quality

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2024 international conference on intelligent computing and data Mining, ICDM 2024

2024 International Conference on Intelligent Computing and D...

引用

2024 international conference on intelligent computing and data Mining, ICDM 2024

ISBN: (纸本)9798331506261

The proceedings contain 28 papers. The topics discussed include: power marketing based on deep reinforcement learning can explain large data mining methods;design and implementation system and statistical model for precision evaluation of measurement methods;research on impact modeling and risk assessment of software requirements change based on dependency structure matrix;improved brain tumor segmentation framework based on multimodal MRI and cascaded segmentation strategy;probabilistic safety verification of stochastic hybrid systems using probably approximately correct barrier certificates;research on electricity price prediction technology based on mode decomposition and deep learning;and a local differential privacy preservation method for high-dimensional data based on big data mining.

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An Overview of Detection of Forest Fires Using intelligent systems 5

An Overview of Detection of Forest Fires Using Intelligent S...

引用

5th ieee international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Choudhary, Vijaya Guha, Paramita Hussain, Shahadat Banga, Alisha Sengupta, Sudhriti Yadav, Shikha Galgotias University UP Greater Noida India CSIR-National Physical Laboratory New Delhi India Bennett University The Times Group UP Greater Noida India Amity Institute of Information Technology Auup UP Greater Noida India

ISBN: (纸本)9798350383522

Forest fire is one of the most common hazards in forests. It not only poses a threat to flora and fauna but to the entire ecology, bio-diversity, and the environment. To prevent any damage from happening, quick action is required. Robots can act as a great first responders in this regard. In this paper, different approaches to the design of mobile robots are discussed. Also, different techniques for the acquisition of data and navigation of robots are elaborated in detail. © 2024 ieee.

关键词： Early Forest fire Detection mobile robots path planning robot locomotion Sensor Nodes Wireless Sensor Networks

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