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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Latha, Dandu Madhavi Anusha, Kondepati Samrin, Rafath Reddy, Pundru Chandra Shaker Sudhakar, Bolleddu Sathish, Goski Geethanjali College of Engineering and Technology Electronics and Communication Engineering Telangana Hyderabad India Marri Laxman Reddy Institute of Technology and Management Computer Science and Engineering Telangana Hyderabad India King Khalid University Department of Computer Science College of Computer Science Asir Abha Saudi Arabia Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Degree Lecturer of Computer Science Mjptbcwreis Telangana India Department of Information Technology Hyderabad India

ISBN: (纸本)9798350372892

When it comes to the IoMT, skin lesion examination is absolutely essential for making an accurate diagnosis. Skin lesion analysis relies heavily on computer-aided design(CAD) technologies to enhance accuracy and efficiency. Using hybrid deep learning approaches, this work focuses on segmenting and classifying skin lesions from dermoscopy images. Mask Region-based Convolutional Neural Network (MRCNN) for semantic separation and ResNet50 for lesion-detection are two state-of-the-art methodologies that are combined in this research's hybrid deep learning model. A skin lesion's exact location can be determined with the use of the MRCNN's border delineation capabilities. For comprehensive model training, we gather a large collection of dermoscopy photos that have been annotated. This dataset is used to train a hybrid deep-learning(DL) model that can capture images' nuanced representations. An accuracy rate of 95.49% in segmenting lesions into different groups demonstrates the model's capability to do so. Another significant improvement over older method is the high reliability and accuracy of skin lesion classification. After being tested on the ISIC2020 Challenge dataset, the model achieved an impressive 96.75% accuracy rate. Classification and segmentation models outperform the state-of-the-art in Internet-of-Medical-Things(IoMT). When it comes to segmenting and classifying skin lesions, this paper's hybrid deep learning approach works wonders. Outperforming the existing gold standards, the results demonstrate that the model can enhance diagnostic accuracy in an IoMT environment. © 2024 IEEE.

关键词： Convolutional neural networks

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Idea23D: Collaborative LMM Agents Enable 3D Model Generation from Interleaved Multimodal Inputs 31

Idea23D: Collaborative LMM Agents Enable 3D Model Generation...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Chen, Junhao Li, Xiang Ye, Xiaojun Li, Chao Fan, Zhaoxin Zhao, Hao Tsinghua University China Tsinghua Shenzhen International Graduate School Tsinghua University China School of Software and Microelectronics Peking University China College of Computer Science Zhejiang University China College of Computer Science and Technology Harbin Engineering University China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing School of Artificial Intelligence Beihang University China

ISBN: (纸本)9798891761964

With the success of 2D diffusion models, 2D AIGC content has already transformed our lives. Recently, this success has been extended to 3D AIGC, with state-of-the-art methods generating textured 3D models from single images or text. However, we argue that current 3D AIGC methods still don't fully unleash human creativity. We often imagine 3D content made from multimodal inputs, such as what it would look like if my pet bunny were eating a doughnut on the table. In this paper, we explore a novel 3D AIGC approach: generating 3D content from IDEAs. An IDEA is a multimodal input composed of text, image, and 3D models. To our knowledge, this challenging and exciting 3D AIGC setting has not been studied before. We propose the new framework Idea23D, which combines three agents based on large multimodal models (LMMs) and existing algorithmic tools. These three LMM-based agents are tasked with prompt generation, model selection, and feedback reflection. They collaborate and critique each other in a fully automated loop, without human intervention. The framework then generates a text prompt to create 3D models that align closely with the input IDEAs. We demonstrate impressive 3D AIGC results that surpass previous methods. To comprehensively assess the 3D AIGC capabilities of Idea23D, we introduce the Eval3DAIGC-198 dataset, containing 198 multimodal inputs for 3D generation tasks. This dataset evaluates the alignment between generated 3D content and input IDEAs. Our user study and quantitative results show that Idea23D significantly improves the success rate and accuracy of 3D generation, with excellent compatibility across various LMM, Text-to-Image, and Image-to-3D models. Code and dataset are available at https://***/. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Network Intrusion Detection System: Machine Learning Approach 2

Network Intrusion Detection System: Machine Learning Approac...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Garg, Ishaan Sharma, Priyansh Singh, Gurveer Sharma, Purushottam Sharma, Vansh Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India School of Computer Science & Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798331519056

The data set contains 25,192 rows and a varied range of features. All the features were found to have an impact on model performance: the data is a mix of numerical and categorical nature;thus, the numerical ones are classified into integers and floats, signifying quantities or connection-level attributes. So much diversity provides further scope for learning from the data. Most of the predictive labels are binary, though protocol type was encoded as multinomial for the latter stages of analysis. The categorical features are encoded using one-hot encoding such that it can be convertible to binary that may make sense for understanding the difference between the output in relation to different categories. Logistic regression was used to get the probability of the presence of a binary dependent variable conditional on the independent variable. It was used in an effort to explain what specific features of such an event would cause it to happen, a common practical use of logistic regression for classification. The model it was computed with utilized standard computational resources and had a size of around 8.1MB of memory, thus making it well-suited to all machines. The experimental results showed an accuracy rate ranging between (100%) as the highest obtained while the average was (97.72%), which was satisfying compared to the other approaches. © 2024 IEEE.

关键词： Network intrusion

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Blockchain-Based Decentralized Application for Telesurgery in Metaverse Environment 14

Blockchain-Based Decentralized Application for Telesurgery i...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Chaudhary, Satya Patel, Yogi Chaudhary, Sachi Gupta, Rajesh Kakkar, Riya Tanwar, Sudeep Nair, Anuja Ahmedabad International School Gujarat Ahmedabad380054 India Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad382481 India

ISBN: (纸本)9798350344837

With the advent of technology in the medical industry, telesurgery has emerged as a transformative era of conventional medical practices to facilitate remote and efficient surgical procedures for the betterment of patients' health. Moreover, innovative technologies introduce a full-immersive metaverse environment for enabling communication between patients and doctors virtually and efficiently. Nevertheless, remote telesurgery operations in the metaverse are susceptible to various security and privacy issues that need to be tackled to secure the patient's confidential health data. Thus, we design an Ethereum blockchain-based decentralized application (DApp) for selecting and conducting telesurgery operations based on the doctors' specialization and type of surgery. The proposed application ensures secure and preserved communication between patient and doctor in the metaverse environment by deploying the smart contract in the Remix Integrated Development Environment (IDE). DApp is designed using web3 and JavaScript (JS) to reflect the telesurgery operations to the doctor and patient in the metaverse environment. Finally, the proposed smart contract for the application is evaluated based on parameters such as gas consumption analysis for smart contract functions and doctors registered and cost analysis for smart contract functions. © 2024 IEEE.

关键词： Smart contract

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IRS-Assisted Hybrid Spatial Modulation for Full-Duplex MIMO Communications 18

IRS-Assisted Hybrid Spatial Modulation for Full-Duplex MIMO ...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Sanila, Kunnathully Sadanandan Rajamohan, Neelakandan Luleå University of Technology Department of Computer Science Electrical and Space Engineering Sweden Indian Institute of Technology School of Electrical Sciences Goa India

ISBN: (纸本)9798350391725

In this paper, we propose an intelligent reflecting surface (IRS)-assisted hybrid transmit-receive spatial modulation (HSM) for full-duplex (FD) multi-input multi-output communication, referred to as FD-IRS-HSM. In the proposed FD-IRS-HSM, the FD communication between two users is accomplished with a dedicated IRS for each user. In addition to the conventional data transfer, transmit and receive spatial modulations are simultaneously employed in the transmission by tuning the reflecting elements in the IRS. Further, we characterize the self-interference power, present the theoretical analysis for ergodic capacity and outage probability for the proposed system, and substantiate it with simulation results. We also compare the proposed FD-IRSHSM with closely related works in the literature and demonstrate the outperformance of our proposed system in terms of higher transmission rate and greater flexibility. © 2024 IEEE.

关键词： Signal modulation

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Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts 41

Long-Tail Learning with Foundation Model: Heavy Fine-Tuning ...

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41st International Conference on Machine Learning, ICML 2024

作者： Shi, Jiang-Xin Wei, Tong Zhou, Zhi Shao, Jie-Jing Han, Xin-Yan Li, Yu-Feng National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Computer Science and Engineering Southeast University China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

The fine-tuning paradigm in addressing long-tail learning tasks has sparked significant interest since the emergence of foundation models. Nonetheless, how fine-tuning impacts performance in long-tail learning was not explicitly quantified. In this paper, we disclose that heavy fine-tuning may even lead to non-negligible performance deterioration on tail classes, and lightweight fine-tuning is more effective. The reason is attributed to inconsistent class conditions caused by heavy fine-tuning. With the observation above, we develop a low-complexity and accurate long-tail learning algorithms LIFT with the goal of facilitating fast prediction and compact models by adaptive lightweight fine-tuning. Experiments clearly verify that both the training time and the learned parameters are significantly reduced with more accurate predictive performance compared with state-of-the-art approaches. The implementation code is available at https://***/shijxcs/LIFT. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Magnetic Resonance Image Analysis: A Healthcare System 4

Magnetic Resonance Image Analysis: A Healthcare System

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

作者： Dubey, Shivendra Singh, Shweta Dubey, Sakshi Nair, Rajit Sharma, Rahul Manipal University School of Engineering Department of Computer Science and Engineering Department of Aiml Rajasthan Jaipur India Jagran Lakecity University School of Engineering and Technology Madhya Pradesh Bhopal India Rkdf University Department of Electronics and Communication Madhya Pradesh Bhopal India Vit Bhopal University Department of Computer Science and Engineering Madhya Pradesh Bhopal India Parul Institute of Technology Parul University Department of Computer Science and Engineering Gujarat Vadodara India

ISBN: (纸本)9798331518981

In today's medical image, analyses are captured very rapidly due to early detection of the brain tumour being very important. The tumour could be plainly visible in the neurological magnetic resonance imaging study, but the doctor must also quantify the tumour area at a certain level in order to conduct additional research. Machine learning has witnessed remarkable consideration over the past few years. From the analysis of medical images to natural language processing, several machine learning models spanning many fields appear to be able to detect tumours and are widely used in applications in academia, business, and medical science. These advancements have a significant impact on healthcare in a broad range of areas, including healthcare, medical imaging technologies, medical diagnostics, and medical data analysis. We give a broad summary of current developments in machine learning used for medical processing and evaluation of images as well as related difficulties. Magnetic Resonance Image (MRI) analysis using IoT involves collecting data from connected medical devices and sensors to create MRI images. Machine learning algorithms can then be used to analyze this data in order to find trends and forecast the health of patients. It can assist medical professionals in making better decisions that will enhance patient care and outcomes. © 2024 IEEE.

关键词： Support vector machines

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A study on the noise reduction method in turbid water environments using Spatial frequency characteristics 9

A study on the noise reduction method in turbid water enviro...

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Koura, Ayumu Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Iizuka city Japan Hankyong National University School of ICT Robotics and Mechanical Engineering Iitc Anseong-si Korea Republic of

ISBN: (纸本)9798350363043

Scattering noise reduction is a challenging project to remove noise under scattering media conditions such as fog or turbid water. In previous study, blurring caused by scattering medium particles in fog or turbid water was removed by low-pass filtering. However, in this method, the accurate color information of the object may be lost due to rough low-frequency filtering. In addition, it is difficult to the obtain object shape information in heavy turbid environments. To solve this problem, we propose a new de-scattering algorithm that combines Fourier-domain filtering and integral imaging by creating a filter tailored to the frequency to be removed. To prove the proposed method, we perform a numerical analysis of experimental results using conventional and proposed methods. © 2024 IEEE.

关键词： Noise abatement

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Rotating machinery fault diagnosis based on residual shrinking convolution and GSoP attention mechanism

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Zhendong yu Chongji/Journal of Vibration and Shock 2025年第13期44卷 277-287页

作者： Liu, Baoluo Li, Chen Nie, Yalin Wang, Guoyong School of Computer and Information Engineering Luoyang Institute of Science and Technology Luoyang471000 China

To address the challenges of extracting effective features from vibration signals in environments with strong background noise and variable load conditions, which results in low fault diagnosis accuracy and poor generalization ability, a rotating machinery fault diagnosis method is proposed that combines residual shrinkage convolution and the Global Second-Order Pooling (GSoP) attention mechanism. It constructs residual shrinkage convolution using soft thresholding filtering technology with multichannel, multi-scale convolution, and integrates attention factors to the filtered signals after soft thresholding filters to suppress irrelevant features and enhance effective features. Furthermore, leveraging high-order statistical modeling concepts, the GSoP attention mechanism is added after the residual shrinkage convolution layer, enhancing the model's ability to extract discriminative features through the second-order statistical information of high-level feature maps. Finally, testing experiments were conducted using the Case Western Reserve University bearing dataset and the University of Connecticut gearbox dataset. The proposed method achieved diagnostic accuracies of 98.87% and 99.64% under 6dB SNR conditions, respectively. Under varying noise and load conditions, the diagnostic performance of the proposed method outperformed that of the comparison models. The experimental results indicate that the proposed method shows good fault identification ability and generalization capability in complex operating environments. © 2025 Chinese Vibration engineering Society. All rights reserved.

关键词： Rotating machinery

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Conflict Between Artificial Intelligence and Traditional Designers: How Artificial Intelligence Transforms the Design Process 3rd

Conflict Between Artificial Intelligence and Traditional Des...

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3rd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Gupta, Keshav Dev Pandey, Rishi Pathak, Devendra Nath Ranjan, Rakesh Gupta, Vijay Department of Computer Science Engineering Poornima College of Engineering Rajsthan Jaipur India Department of Animation & Multimedia Birla Institute of Technology Ranchi Mesra India Department of Computer Science & Engineering Poornima College of Engineering ISI-6 RIICO Institutional Area Sitapura Rajasthan Jaipur India Department of Computer Science & Engineering Babu Banarasi Das University Lucknow India Department of Computer Science International School of Informatics and Management Sector 12 Mahaveer Marg Jaipur Mansarovar India

ISBN: (纸本)9789819739905

The development of technology, especially the use of resources provided by artificial intelligence (AI), has advanced in areas where business is gaining importance and is an important fear of economic growth. The use of technical skills in the creation of products and services is not unique to design. In today's world, artificial intelligence (AI) has become an industry in many countries, and design is no exception. However, the lines of work performed by humans and machines are changing rapidly, and as a result, the global market is also changing a lot. This is the result of very rapidly developing technology. This raises the question of how this progress has and will continue to influence the future work of designers. What skills do designers need to enter or stay in this industry? The purpose of this article is to provide a summary of research findings on the impact of intelligence on the performance of designers, as well as an analysis that includes an evaluation of the potential and benefits of intelligent design solutions. Case studies and other scientific studies are used to analyze the applications and benefits of AI-based solutions. Studies can be theoretical or empirical. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： International trade

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