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Analyzing the impact of machine learning and artificial intelligence and its effect on management of lung cancer detection in covid-19 pandemic

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Materials Today: Proceedings 2022年 56卷 2213-2216页

作者： Boddu, Raja Sarath Kumar Karmakar, Partha Bhaumik, Ankan Nassa, Vinay Kumar Vandana Bhattacharya, Sumanta Department of CSE Lenora College of Engineering Andhra Pradesh Rampachodavaram India Government of West Bengal Bikash Bhawan Salt Lake W.B. Kolkata India Dept. of Applied Mathematics With Oceanology and Computer Programming Vidyasagar University Midnapore India Department of Computer Science Engineering South Point Group of Institutions Sonepat Haryana India Department of Mathematics Dasmesh Khalsa College Zirakpur India Department of Science and Technology and Biotechnology MAKAUT India

Cancer victims, particularly those with lung cancer, are more susceptible and at higher danger of COVID-19 and associated consequences as a result of their compromised immune systems, which makes them particularly sensitive. Because of a variety of circumstances, cancer patients' diagnosis, treatment, and aftercare are very complicated and time-consuming during an epidemic. In such circumstances, advances in artificial intelligence (AI) and machine learning algorithms (ML) offer the capacity to boost cancer sufferer diagnosis, therapy, and care via the use of cutting technologies. For example, using clinical and imaging data combined with machine learning methods, the researchers may be able to distinguish among lung alterations induced by corona virus and those produced by immunotherapy and radiation. During this epidemic, artificial intelligence (AI) may be utilized to guarantee that the appropriate individuals are recruited in cancer clinical trials more quickly and effectively than in the past, which was done in a conventional and complicated manner. In order to better care for cancer patients and find novel and more effective therapies, It is critical that we move beyond traditional research methods and use artificial intelligence (AI) and machine learning to update our research (ML). Artificial intelligence (AI) and machine learning (ML) are being utilised to help with several aspects of the COVID-19 epidemic, such as epidemiology, molecular research and medication development, medical diagnosis and treatment, and socioeconomics. The use of artificial intelligence (AI) and machine learning (ML) in the diagnosis and treatment of COVID-19 patients is also being investigated. The combination of artificial intelligence and machine learning in COVID-19 may help to identify positive patients more quickly. In order to understand the dynamics of an epidemic that is relevant to artificial intelligence, when used in different patient groups, AI-based algorithms can quic

关键词： COVID-19

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Hypergraph-enhanced Dual Semi-supervised Graph Classification 41

Hypergraph-enhanced Dual Semi-supervised Graph Classificatio...

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41st International Conference on Machine Learning, ICML 2024

作者： Ju, Wei Mao, Zhengyang Yi, Siyu Qin, Yifang Gu, Yiyang Xiao, Zhiping Wang, Yifan Luo, Xiao Zhang, Ming School of Computer Science National Key Laboratory for Multimedia Information Processing Peking University-Anker Embodied AI Lab Peking University China School of Statistics and Data Science Nankai University China Department of Computer Science University of California Los Angeles United States School of Information Technology & Management University of International Business and Economics China

In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unlabeled graphs. Despite the promising capability of graph neural networks (GNNs), they typically require a large number of costly labeled graphs, while a wealth of unlabeled graphs fail to be effectively utilized. Moreover, GNNs are inherently limited to encoding local neighborhood information using message-passing mechanisms, thus lacking the ability to model higher-order dependencies among nodes. To tackle these challenges, we propose a Hypergraph-Enhanced DuAL framework named HEAL for semi-supervised graph classification, which captures graph semantics from the perspective of the hypergraph and the line graph, respectively. Specifically, to better explore the higher-order relationships among nodes, we design a hypergraph structure learning to adaptively learn complex node dependencies beyond pairwise relations. Meanwhile, based on the learned hypergraph, we introduce a line graph to capture the interaction between hyperedges, thereby better mining the underlying semantic structures. Finally, we develop a relational consistency learning to facilitate knowledge transfer between the two branches and provide better mutual guidance. Extensive experiments on real-world graph datasets verify the effectiveness of the proposed method against existing state-of-the-art methods. Copyright 2024 by the author(s)

关键词： Knowledge graph

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Blockchain-based color medical image cryptosystem for industrial Internet of Healthcare Things (IoHT)

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Multimedia Tools and Applications 2024年 1-55页

作者： Khallaf, Fatma El-Shafai, Walid El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Ahram Canadian University 6th October City Giza Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

In recent years, the proliferation of smart devices and associated technologies, such as the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Internet of Medical Things (IoMT), has witnessed a substantial growth. However, the limited processing power and storage capacity of smart devices make them vulnerable to cyberattacks, rendering traditional security and cryptography techniques inadequate. To address these challenges, blockchain (BC) technology has emerged as a promising solution. This study introduces an efficient framework for the Internet of Healthcare Things (IoHT), presenting a novel cryptosystem for color medical images using BC technology in conjunction with the IoT, Secure Hash Algorithm 256-bit (SHA256), shuffling, and bitwise XOR operations. The encryption scheme is specifically designed for an IIoT grid network computing system, relying on diffusion and confusion principles. In this paper, the proposed cryptosystem strength is evaluated against differential attacks with several comprehensive metrics. Simulation results and theoretical analysis demonstrate the cryptosystem effectiveness, showcasing its ability to provide high levels of security and immunity to data leakage. The proposed cryptosystem offers a versatile range of technical solutions and strategies that are adaptable to various scenarios. The evaluation metrics, with approximate values of 99.61% for Number of Pixels Change Rate (NPCR), 33.46% for Unified Average Changed Intensity (UACI), and 8 for information entropy, closely align with the desired ideal outcomes. Consequently, this paper contributes to the advancement of secure and private systems for medical image encryption based on BC technology, potentially mitigating the risks associated with cyberattacks on smart medical devices. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Evaluation of Computation Overhead of Paillier Encryption in Vertical Federated Learning

Evaluation of Computation Overhead of Paillier Encryption in...

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India Council International Subsections Conference (INDISCON), IEEE

作者： P Abhinand Tomsy Paul G Santhosh Kumar Department of Computer Science Cyber Physical Systems Lab Cochin University of Science and Technology Kochi India

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

Paillier Encryption is one of the most commonly used Partially Homomorphic Encryption (PHE) techniques. Since it is additive and also faster compared with fully homomorphic techniques, it is used extensively in Vertical Federated Learning (VFL) where encryption is required to secure the exchange of gradients and partial results between parties. However, the computation associated with Paillier encryption is very huge. We present its computation overhead in the context of VFL for various machine learning models. Specifically, we find the number of encryption/decryption and homomorphic operations such as addition and scalar multiplication required in various machine learning models and the time of execution associated with each. We see that as the number of iterations increases, these operations have a severe impact on the total time for learning.

关键词： Additives Federated learning Computational modeling Homomorphic encryption Context modeling

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Kalman Filter based Attack Detection for Adaptive Cruise Control Systems 5

Kalman Filter based Attack Detection for Adaptive Cruise Con...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Yang, Nachuan Nan, Fuhai Revadigar, Girish Shi, Ling Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Hong Kong Huawei Singapore Research Center Shield Lab Singapore

ISBN: (纸本)9798350325294

In this paper, we investigate the security issues of adaptive cruise control (ACC) systems for autonomous vehicles. Different from the research on cyber-layer security, we focus on cyber-physical system (CPS) security which incorporates the vehicle's control and dynamics into the security design. More specifically, we first analyze the longitudinal dynamics of on-road vehicles and then formulate a leader-follower model for the ACC system. Moreover, we establish a continuous-time Kalman filter for the leader-follower model and characterize its innovation's probabilistic properties. Finally, we develop multiple attack detectors based on Kalman filtering and statistical testing. The effectiveness of the proposed methods is validated via numerical simulations and experiments on Carla, an open-source platform for autonomous driving research. © 2023 IEEE.

关键词： Numerical methods

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LEARNING WITH LOGICAL CONSTRAINTS BUT WITHOUT SHORTCUT SATISFACTION 11

LEARNING WITH LOGICAL CONSTRAINTS BUT WITHOUT SHORTCUT SATIS...

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11th International Conference on Learning Representations, ICLR 2023

作者： Li, Zenan Liu, Zehua Yao, Yuan Xu, Jingwei Chen, Taolue Ma, Xiaoxing Lü, Jian State Key Lab of Novel Software Technology Nanjing University China Department of Mathematics The University of Hong Kong Hong Kong Department of Computer Science Birkbeck University of London United Kingdom

Recent studies have explored the integration of logical knowledge into deep learning via encoding logical constraints as an additional loss ***, existing approaches tend to vacuously satisfy logical constraints through shortcuts, failing to fully exploit the *** this paper, we present a new framework for learning with logical ***, we address the shortcut satisfaction issue by introducing dual variables for logical connectives, encoding how the constraint is *** further propose a variational framework where the encoded logical constraint is expressed as a distributional loss that is compatible with the model's original training *** theoretical analysis shows that the proposed approach bears salient properties, and the experimental evaluations demonstrate its superior performance in both model generalizability and constraint satisfaction. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Signal encoding

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Few-Shot Vision-Language Action-Incremental Policy Learning

arXiv

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arXiv 2025年

作者： Song, Mingchen Deng, Xiang Zhong, Guoqiang Lv, Qi Wan, Jia Li, Yinchuan Hao, Jianye Guan, Weili School of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China Department of Computer Science and Technology Ocean University of China China Huawei Noah’s Ark Lab China

Recently, Transformer-based robotic manipulation methods utilize multi-view spatial representations and language instructions to learn robot motion trajectories by leveraging numerous robot demonstrations. However, the collection of robot data is extremely challenging, and existing methods lack the capability for continuous learning on new tasks with only a few demonstrations. In this paper, we formulate these challenges as the Few-Shot Action-Incremental Learning (FSAIL) task, and accordingly design a Task-prOmpt graPh evolutIon poliCy (TOPIC) to address these issues. Specifically, to address the data scarcity issue in robotic imitation learning, TOPIC learns Task-Specific Prompts (TSP) through the deep interaction of multi-modal information within few-shot demonstrations, thereby effectively extracting the task-specific discriminative information. On the other hand, to enhance the capability for continual learning on new tasks and mitigate the issue of catastrophic forgetting, TOPIC adopts a Continuous Evolution Strategy (CES). CES leverages the intrinsic relationships between tasks to construct a task relation graph, which effectively facilitates the adaptation of new tasks by reusing skills learned from previous tasks. TOPIC pioneers few-shot continual learning in the robotic manipulation task, and extensive experimental results demonstrate that TOPIC outperforms state-of-the-art baselines by over 26% in success rate, significantly enhancing the continual learning capabilities of existing Transformer-based policies1 Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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LEARNING CONTINUOUS NORMALIZING FLOWS FOR FASTER CONVERGENCE TO TARGET DISTRIBUTION VIA ASCENT REGULARIZATIONS 11

LEARNING CONTINUOUS NORMALIZING FLOWS FOR FASTER CONVERGENCE...

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11th International Conference on Learning Representations, ICLR 2023

作者： Chen, Shuangshuang Ding, Sihao Karayiannidis, Yiannis Björkman, Mårten School of Electrical Engineering and Computer Science Royal Institute of Technology Sweden AI Lab Volvo Car Corporation Sweden Department of Automatic Control Lund University Sweden

Normalizing flows (NFs) have been shown to be advantageous in modeling complex distributions and improving sampling efficiency for unbiased *** this work, we propose a new class of continuous NFs, ascent continuous normalizing flows (ACNFs), that makes a base distribution converge faster to a target *** solving such a flow is non-trivial and barely possible, we propose a practical implementation to learn flexibly parametric ACNFs via ascent regularization and apply it in two learning cases: maximum likelihood learning for density estimation and minimizing reverse KL divergence for unbiased sampling and variational *** learned ACNFs demonstrate faster convergence towards the target distributions, therefore, achieving better density estimations, unbiased sampling and variational approximation at lower computational ***, the flows show to stabilize themselves to mitigate performance deterioration and are less sensitive to the choice of training flow length T. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Maximum likelihood estimation

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Subjective Time Estimation to Measure the Cognitive Load of Interactive Mobile User Interfaces

Subjective Time Estimation to Measure the Cognitive Load of ...

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Anamalamudi, Komala Surampudi, Bapi Raju Peddapalyam, Purushotham Modin, Rasheed Ahamed School of Computer and Information Sciences University of Hyderabad Hyderabad India Madanapalle Institute of Technology and Science Department of CSE Madanapalle India IIIT-Hyderabad Cognitive Science Lab Hyderabad India Senior UI/UX Designer Xoxoday Bengaluru India Madanapalle Institute of Technology and Science Department of AI and Data Science Madanapalle India

ISBN: (纸本)9798350335835

Measuring subjective experience of time helps the designer of the product to understand the usability of the product and the user experience. Time is not given it's due importance in HCI and particularly in the usability design of the products. Though it is evident that cognitive load impacts time/duration estimation, thereby the user creates a positive or negative valence towards the product, there is no much research done to measure the usability of the products with cognitive load and duration judgement as dependent variables. This paper focuses on empirical investigation of prospective duration judgement as a measure to assess the cognitive load experienced by the users while using the product, one-on-one mentoring mobile app. We consider Jakob Nielsen's heuristics and meCUE questionnaire as the anchors for our empirical investigation. © 2023 IEEE.

关键词： Human computer interaction

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An Iterative Optimizing Framework for Radiology Report Summarization With ChatGPT

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4163-4175页

作者： Ma, Chong Wu, Zihao Wang, Jiaqi Xu, Shaochen Wei, Yaonai Liu, Zhengliang Zeng, Fang Jiang, Xi Guo, Lei Cai, Xiaoyan Zhang, Shu Zhang, Tuo Zhu, Dajiang Shen, Dinggang Liu, Tianming Li, Xiang Northwestern Polytechnical University School of Automation Xi'an710072 China University of Georgia School of Computing AthensGA30602 United States Northwestern Polytechnical University School of Computer Science Xi'an710072 China Massachusetts General Hospital Department of Radiology BostonMA02114 United States University of Electronic Science and Technology of China Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology Chengdu611731 China The University of Texas at Arlington Department of Computer Science and Engineering ArlingtonVA76019 United States ShanghaiTech University School of Biomedical Engineering Shanghai201210 China Shanghai United Imaging Intelligence Company Ltd. Department of Research and Development Shanghai200030 China Shanghai Clinical Research and Trial Center Shanghai201210 China

The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians. Typically written by radiologists, this part is derived from the 'Findings' section, which can be laborious and error-prone. Although deep-learning-based models, such as bidirectional encoder representation from transformers (BERT), have achieved promising results in automatic impression generation (AIG), such models often require substantial amounts of medical data and have poor generalization performance. Recently, large language models (LLMs) like Chat Generative Pre-trained Transformer (ChatGPT) have shown strong generalization capabilities and performance, but their performance in specific domains, such as radiology, remains under-investigated and potentially limited. To address this limitation, we propose ImpressionGPT, leveraging the contextual learning capabilities of LLMs through our dynamic prompt and iterative optimization algorithm to accomplish the AIG task. ImpressionGPT initially employs a small amount of domain-specific data to create a dynamic prompt, extracting contextual semantic information closely related to the test data. Subsequently, the iterative optimization algorithm automatically evaluates the output of LLMs and provides optimization suggestions, continuously refining the output results. The proposed ImpressionGPT model achieves superior performance of AIG task on both the Medical Information Mart for Intensive Care - Chest X-ray database (MIMIC-CXR) and Open Access Biomedical Image Search Engine (OpenI) datasets without requiring additional training data or fine-tuning the LLMs. This work presents a paradigm for localizing LLMs that can be applied in a wide range of similar application scenarios, bridging the gap between general-purpose LLMs and the specific language processing needs of various domains. © 2020 IEEE.

关键词： Semantics

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