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Optimization Algorithm of Multi-Attribute Group Decision-Making Based on Information Entropy Theory 4

Optimization Algorithm of Multi-Attribute Group Decision-Mak...

引用

4th international conference on Industrial Automation, robotics and Control engineering, IARCE 2024

作者： Feng, Xuan Cai, Youbao Institute of Computer Science Sichuan Province Chengdu China

ISBN: (纸本)9798350380323

The problems of complex mathematical models, high computational complexity, and repeated parameter adjustments in the subjective and objective weighting method is studied. Such problems are generally defined as multi-attribute group decision-making models. Multi-attribute group decision-making models have been widely applied in fields such as achievement evaluation, scientific research performance assessment, and project review, and are a popular research direction in the field of artificial intelligence. How to balance the subjectivity among experts and the objectivity of attribute weights is a challenging problem in the research of multi-attribute group decision-making models. The entropy weighting method is firstly used to obtain the objective weights of attributes. Then, the grey correlation degree method is applied to adjust the subjective weight. Finally, based on the minimum Kullback-Leibler divergence method criterion reflecting the distance between different weights, an optimization model is established to solve the integrated weight. It is provided a feasible weight integration method based on information entropy theory for multi-attribute group decision-making. A numerical experiment shows the feasibility and practicability of the method. It is provided a new solution to the problems of prioritizing quantity over quality in the performance evaluation of scientific research in universities and research institutions, which can effectively promote the enthusiasm of scientific and technological workers for scientific research innovation. © 2024 IEEE.

关键词： Optimization algorithms

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SecureML: Machine Learning-Powered Malware Detection 2

SecureML: Machine Learning-Powered Malware Detection

引用

2nd IEEE international conference on advances in Information technology, ICAIT 2024

作者： Awasthi, Animay Ahsan, Mohd Vanusha, D. Delsi Robinsha, S. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu District Tamilnadu Kattankulathur603 203 India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chengalpattu District Tamilnadu Kattankulathur603 203 India

ISBN: (纸本)9798350383867

As malware threats become increasingly complex, the development of advanced detection systems is necessary to protect digital assets. SecureML Using Machine Learning presents an innovative approach to combat the escalating threat of malicious software by leveraging machine learning algorithms. This research paper outlines the design, implementation, and evaluation of SecureML, emphasizing its effectiveness in detecting both known and novel malware strains. Through comprehensive feature selection techniques and algorithmic comparison, SecureML identifies crucial features and selects the Random Forest algorithm as the most effective for malware detection, achieving an impressive accuracy rate of 99.40% on the test set. The system's low false positive and false negative rates validate its robustness and reliability in accurately distinguishing between benign and malicious files. SecureML demonstrates significant potential in bolstering cybersecurity defenses and safeguarding digital assets against evolving malware threats. © 2024 IEEE.

关键词： Adversarial machine learning

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De-noising of Low Dose CT Liver Images Using Improved Discrete Wavelet Transform 7th

De-noising of Low Dose CT Liver Images Using Improved Discre...

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Maria, H. Heartlin Kayalvizhi, R. Evangeline, I. Keren Princess, T. George Mangalya, T. Rashmika Prakash, J. Shakthi Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Robotics and Automation SRM Easwari Engineering College Chennai India Department of Medical Electronics SRM Valliammai Engineering College Kattankulathur India Department of Artificial Intelligence and Datascience St. Joseph’s Institute of Technology Chennai India

ISBN: (纸本)9783031699818

CT scans have become increasingly common in recent years and are essential for diagnosing various medical conditions. But the most significant element influencing a CT image’s quality is noise, which frequently taints low-dose scans. The radiologists’ choice may then be impacted by this. Therefore, in order to enhance visibility and increase the clarity of the image, LDCT images must be removed of noise before being utilized for diagnosis. This work is one such attempt to remove noise from low dose Computed Tomography (CT) liver images. In the present work, the noise from the CT liver images is eliminated using a modified discrete wavelet transform (DWT). The scheme system is used in conjunction with the DWT. The lifting approach improves the wavelet transform’s accuracy, simplicity, efficiency, and flexibility. Furthermore, the noisy wavelet coefficients are denoised via Bayesian shrinkage. The de-noised image PSNR, MSE, SSIM, and SNR values are computed, and a comparison with the traditional de-noising methods is carried out. The quantitative study reveals that the suggested approach has produced noteworthy outcomes when compared to the efficacy of conventional procedures. Furthermore, as the results and discussion section demonstrate, the suggested lifting technique in conjunction with the DWT minimizes the memory needs while simultaneously reducing complexity. © IFIP international Federation for Information Processing 2024.

关键词： Discrete wavelet transforms

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A Survey on Deep Learning Methods for Speech Emotion Recognition 14

A Survey on Deep Learning Methods for Speech Emotion Recogni...

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14th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Lakshmi, K.C. Sharath, M.N. Harshitha, S. Arjun, B.C. Department of Computer Science and Engineering Rajeev Institute of Technology Hassan India Department of Computer Science and Engineering Malnad College of Engineering Hassan India Department of Information Science and Engineering Rajeev Institute of Technology Hassan India

Speech Emotion Recognition is a system that can recognize the emotion in various audio samples. Research in recent years has concentrated on applying deep learning to speech-related applications. This article provides an overview of deep learning techniques and examines work that uses these techniques to identify speech emotions. In this study, speech signals are used to analyze basic emotions such as calm, enjoyment, fear, disgust, etc. Review topics include the database employed, the emotions extracted, and the advancements made in speech emotion identification. © Grenze Scientific Society, 2023.

关键词： Deep learning

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Extracting, Transforming, and Analyzing Olympic Data Using Azure Services: A Case Study

Extracting, Transforming, and Analyzing Olympic Data Using A...

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2024 international conference on advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Samanta, Debajit Roy, Subhodeep Sarkar, Pratima Misra, Bitan Techno International New Town Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350388800

A field of investigation and the foundation of cloud computing are employed to administer a common resource pool. Analytics techniques are required in educational mining because of the massive amount of data needed to obtain relevant knowledge. As digital data repositories grow and dispersion, sophisticated data analysis techniques are required to quickly extract relevant knowledge from them. A cloud platform created as a service pipeline for creating and implementing distributed data analytics applications. In this scenario, we make use of learning models, data sets, analysis instruments, and data mining techniques that can be deployed as stand-alone services that can run on clouds. In this study the central focus lies in establishing a robust data pipeline utilizing Azure services, specifically targeting the Tokyo Olympic dataset obtained from Kaggle. The initial phase involves the extraction of relevant information from the Kaggle dataset using Azure Data Factory, a cloud-based integration service. Following this, Azure Databricks, featuring Apache Spark functionality, is employed for the transformation of the Tokyo Olympic data. The primary objective remains to enable advanced analytics, focusing on the Tokyo Olympic dataset. The utilization of Azure Synapse Analytics empowers organizations to perform intricate SQL queries on the Tokyo Olympic data, revealing latent trends and correlations. The outcomes can be visualized through SQL-based approaches in conjunction with Azure Synapse Analytics, providing a comprehensive understanding of the Tokyo Olympic dataset for well- informed decision-making. © 2024 IEEE.

关键词： Cloud platforms

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Early Detection and Treatment of Skin Diseases Using Advanced Image Preprocessing and Machine Learning Techniques 4

Early Detection and Treatment of Skin Diseases Using Advance...

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4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Pandian, Jeswin Paul Jegan, R. Jerlinkasmir, R. Jose, P. Subha Hency Raj, P. Anantha Christu Karunya Institute of Technology and Science Division of Biomedical Engineering Coimbatore India Karunya Institute of Technology and Science Department of Mathematics Coimbatore India Karunya Institute of Technology and Science Division of Robotics and Automation Engineering Coimbatore India

ISBN: (纸本)9798331529635

This research aims to develop a more accurate and efficient system for classifying skin diseases, including both malignant and non-cancerous conditions. The study utilizes a comprehensive dataset encompassing various skin diseases, including melanomas, basal cell carcinomas, and benign conditions like nevi and warts. To address the challenge of class imbalance with a higher number of melanoma images, the researchers employ techniques like SMOTE (Synthetic Minority Oversampling Technique) to create synthetic data for under-represented classes. The pre-processing stage involves crucial steps like image scaling, augmentation, normalization, and data cleaning to ensure the quality and consistency of the data. Following pre-processing, feature extraction and model training will be conducted. This research has the potential to significantly contribute to the field of dermatology by enabling faster and more precise diagnosis of skin diseases through the use of advanced pre-processing techniques and a diversified dataset. © 2024 IEEE.

关键词： Lung cancer

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Skin Disease Classification using Transfer Learning with a Modified ResNet-50 Architecture

Skin Disease Classification using Transfer Learning with a M...

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2024 international conference on advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Priya Bhaminie, S. Yogesh, C. School Of Computer Science And Engineering Vellore Institue Of Technology-Chennai Chennai India

ISBN: (纸本)9798350364828

This paper presents an advanced approach in skin disease classification using a modified ResNet-50 architecture, applied to a specific subset from the ISIC 2019 Dataset focusing on Benign Keratosis, Basal Cell Carcinoma, and Melanoma. The study integrates deep learning and medical imaging to enhance diagnostic accuracy in dermatology. It leverages transfer learning, adapting the well-established ResNet-50 model, and modifies it by incorporating global average pooling and dense layers, thus tailoring it for dermatological use. This method is inspired by significant strides in skin cancer detection using ResNet and innovative transfer learning techniques in skin disease classification. A notable aspect of this research is the evaluation of data augmentation's impact on model generalization and robustness. The findings of this study contribute to the growing field of deep learning in skin disease classification, providing a refined and effective tool for potential applications in dermatology. © 2024 IEEE.

关键词： Dermatology

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BEYOND THE PAPER: EXPLORING THE RELATIONSHIP BETWEEN THE PARTICIPATION OF WOMEN AND THE SOCIAL RELEVANCE OF RESEARCH TOPICS IN engineering DESIGN

BEYOND THE PAPER: EXPLORING THE RELATIONSHIP BETWEEN THE PAR...

引用

ASME 2024 international Design engineering Technical conferences and Computers and Information in engineering conference, IDETC-CIE 2024

作者： Running, Ivyann O. Parkinson, Bethany Magleby, Spencer Toh, Christine Brigham Young University ProvoUT United States University of Nebraska Omaha OmahaNE United States

ISBN: (纸本)9780791888407

The increase in participation of women in science, technology, engineering, and Mathematics (STEM) fields has not been uniform. In mechanical engineering, the proportion of women who participate in the profession at all levels is disproportionately low. One factor identified by education researchers that has the potential to draw higher numbers of women to the discipline is the presence of socially relevant and impactful applications of science concepts. To better understand this relationship, the gender distribution of researchers participating in sub-conferences within the mechanical engineering international Design engineering Technical conferences & Computer and Information in engineering (IDETC-CIE) was investigated. The social relevance of publications within two sub-conferences with differing levels of women participants, the "Design Theory and Methodology" (DTM) and "Mechanisms and robotics conference" (MR), was compared. Our results showed that both sub-conferences had comparable numbers of socially relevant papers, but differed in terms of which categories of social relevance were emphasized and if specific populations were explicitly mentioned in the studies. These results indicate that the relationship between the participation of women in engineering research communities and the focus on socially relevant applications is complex and multifaceted, potentially affected by not just if socially relevant concepts are studied, but also how these concepts are integrated into the shared values and norms of each community. Copyright © 2024 by ASME.

关键词： STEM (science, technology, engineering and mathematics)

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Multi-Attention Fusion Lightweight Single Object Tracking Algorithm 7

Multi-Attention Fusion Lightweight Single Object Tracking Al...

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7th international conference on robotics, Control and Automation engineering, RCAE 2024

作者： Bao, Juntao Zhao, Jixing Liu, Bingyou Zhang, Dongxiang Anhui Polytechnic University Dept. School of Electrical Engineering Wuhu China Huaneng Chaohu Power Generation Co. Ltd. Dept. R&d Department ChaoHu China Anhui Tianbing Electronic Technology Co. Ltd. Dept. R&d Department Wuhu China

ISBN: (纸本)9798350355642

With the continuous development of mobile application platforms, balancing real-time performance and accuracy has become a major challenge for object tracking technology, especially on resource-limited mobile devices. To address these challenges, a novel tracking algorithm called NanoMViT (Nano Mobile Vision Transformer for Object Tracking) and the ECSA (Efficient Channel and Spatial Attention) multi-attention module were proposed. By optimizing the backbone network architecture, the parameters of each module were significantly reduced. The final algorithm has a parameter count of only 709.134k and MACs of only 264.127M. Compared to the baseline algorithm, the parameter count is reduced by 66.3%, and the computational load is reduced by 50.2%. To validate the algorithm's performance, comprehensive evaluations were conducted on multiple standard tracking datasets, including VOT2018, DTB70, and LaSOT. The results show that the algorithm outperforms the baseline in aspects such as rapid deformation, tracking of similar objects, and performance under complex lighting conditions. These findings demonstrate that the algorithm has high practicality and efficiency in mobile tracking applications, but further validation is required in more complex environments. © 2024 IEEE.

关键词： Prisms

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Joint Space Control of Aerial Manipulator in Flight using Optimized PD Controller 23

Joint Space Control of Aerial Manipulator in Flight using Op...

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6th international conference on advances in robotics, AIR 2023

作者： Bhandari, Garima Pathak, Pushparaj Mani Indian Institute of Technology Roorkee Uttarakhand Roorkee India

ISBN: (纸本)9781450399807

This research paper presents a model for the dynamics of an aerial manipulator consisting of a planar 2R manipulator attached to a quadcopter with an H-configuration. This research aims to develop a control strategy to ensure stable manipulation while the aerial manipulator is in flight. The paper presents an optimized PD controller for controlling joint space and a near-optimal parameter tuning procedure using the Broyden-Fletcher-Goldfarb-Shanno algorithm. Numerical experimental results show that the quadcopter can reach the desired position and the manipulator can manipulate in joint space, thus proving the effectiveness of the proposed control strategy. This research contributes to developing control strategies for aerial manipulators and provides a basis for future research. © 2023 ACM.

关键词： Manipulators

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