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Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural networks

computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

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A KUSHO WORDS RECOGNITION METHOD FOR CONTACTLESS information MANAGEMENT IN RESTAURANT KITCHENS 51

A KUSHO WORDS RECOGNITION METHOD FOR CONTACTLESS INFORMATION...

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51st International Conference on computers and Industrial engineering, CIE 2024

作者： Usuki, Risa Usuki, Jun Department of Computer Science and Engineering Waseda University Tokyo Japan Department of Information Network and Communication Kanagawa Institute of Technology Kanagawa Japan

Advances in IT technology have led to research into KUSHO recognition, which enables the non-contact operation of devices without the use of devices such as keyboards or controllers. This is a technology that transmits information about the characters written in space towards the camera (hereafter this action is referred to as KUSHO) to the devices. KUSHO has advantages, such as the ability to transfer information without training. In restaurant kitchens, workers transmit various types of information using devices with input functions, however, hygiene considerations are essential when operating equipment in kitchens. In addition, in noisy kitchens, it is also difficult to use voice input, and there are limits to paper-based information transmission. Under these circumstances, in this research, the introduction of KUSHO input technology for non-contact information transfer in restaurant kitchens is being investigated. Therefore, the content handled by kitchen workers for information management is first analyzed and shown to be representable in three input formats. Subsequently, an additional hand signal to be used while doing KUSHO and an improved algorithm for determining them are proposed. Finally, an algorithm for KUSHO recognition of character strings using standard input time is proposed. Experiments on the implementation of these proposals have shown the effectiveness of the new decision algorithm for the addition of a hand signal, with a 100% correct rate. For strings of two or more uppercase alphabetic characters, the recognition rate was 92.5% for all eight types of "KUSHO operation" and the recognition rate was 70.0% for ten randomly selected from 100 different "product names." For "quantity," it was difficult to recognize multiple digits, however 100% recognition rate was achieved by dividing the digits into each single digit 0~9 using an improved hand signal algorithm. Thus, algorithm some problems remain, the effectiveness of all the proposed methods ha

关键词： information management

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Reversible Data Hiding in Shared Images Based on Syndrome Decoding and Homomorphism

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IEEE Transactions on Cloud Computing 2023年第3期11卷 3085-3098页

作者： Xiong, Lizhi Han, Xiao Yang, Ching-Nung Zhang, Xinpeng Nanjing University of Information Science and Technology School of Computer Science Jiangsu Nanjing210044 China Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education CICAEET Jiangsu Nanjing210044 China National Dong Hwa University Department of Computer Science & Information Engineering Hualien974 Taiwan Fudan University School of Computer Science Shanghai200433 China

Reversible Data Hiding in Encrypted Images (RDHEI) has drawn increasing concern in multimedia cloud computing scenarios. It embeds secret message into the encrypted carrier while preserving the confidentiality of the image. However, most RDHEI schemes have only one hider and one image carrier which are not efficient for the distributed system with multiple participants. Moreover, if the stego-image is lost, the cover image and the embedded data cannot be restored. To solve above issues, this paper proposes Reversible Data Hiding in Shared Images based on syndrome decoding and homomorphism (RDHSI). In RDHSI, the cover image and secret data are distributed using secret sharing. Multiple data hiders embed shared data into the shared images based on the Syndrome Decoding of Hamming codes and the additive homomorphism of the polynomial. On the receiver side, the lossless cover image and secret data are obtained. The reversibility and privacy preserving of the cover image, the complete extraction of secret data, and the fault-tolerance of the proposed schemes are achieved. The experimental results show that PSNR of the marked image is above 54 dB at the embedding rate of 0.43 bpp. In order to improve the payload of secret data, the Modified RDHSI is further proposed. Generally, the proposed schemes are secure, effective and fault-tolerant. © 2013 IEEE.

关键词： Data privacy

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Developing creative material in STEM courses using integrated engineering design based on APOS theory

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International Journal of Technology and Design Education 2023年第4期33卷 1627-1651页

作者： Zhong, Hua-Xu Lai, Chin-Feng Chang, Jui-Hung Chiu, Po-Sheng Department of Engineering Science National Cheng Kung University 1 University Rd. Tainan701 Taiwan Computer and Network Center Department of Computer Science and Information Engineering Department of Computer Science National Cheng Kung University 1 University Rd. Tainan701 Taiwan Department of E-Learning Design and Management National Chiayi University No. 85 Wenlong Minxiong Township Chiayi County 621 Taiwan

Over the past several years, creativity has been recognized as an important skill for success in STEM education, engineering design and computational thinking. There is limited research on how to apply Conceive, Design, Implement, Operate (CDIO) engineering design in STEM courses and how it affects students' creativity. This study strategically integrated computational thinking (CT), STEM, and CDIO engineering design, based on Action-Process-Object-Schema (APOS) theory, into a course. This study has adopted a framework to design creative learning materials and examined how this framework impacts students' creativity. This study used a pretest–posttest nonequivalent-groups design. The study investigated a STEM course which had 40 participants (n = 40, male = 11, female = 29) for 12 weeks. 6 weeks were spent using traditional teaching and 6 weeks integrating a CDIO framework into computational thinking. All the students in the course were college students with teacher college backgrounds. To examine the impact of our course, we used a questionnaire to examine the students’ creativity before and after the STEM course was applied. Our results show that our course had a significantly impact on our students’ creativity, particularly in the case of the male students. This finding is consistent with the findings of other research studies. This study also offers some suggestions for teachers who wish to improve their learning materials. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.

关键词： Students

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DP-RAE: A Dual-Phase Merging Reversible Adversarial Example for Image Privacy Protection 24

DP-RAE: A Dual-Phase Merging Reversible Adversarial Example ...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Jiajie Du, Xia Zhou, Jizhe Pun, Chi-Man Xu, Qizhen Liu, Xiaoyuan School of Computer and Information Engineering Xiamen University of Technology Xiamen China School of Computer Science Engineering Research Center of Machine Learning and Industry Intelligence Sichuan University Chengdu China Department of Computer and Information Science University of Macau China

ISBN: (纸本)9798400706868

In digital security, Reversible Adversarial Examples (RAE) blend adversarial attacks with Reversible Data Hiding (RDH) within images to thwart unauthorized access. Traditional RAE methods, however, compromise attack efficiency for the sake of perturbation concealment, diminishing the protective capacity of valuable perturbations and limiting applications to white-box scenarios. This paper proposes a novel Dual-Phase merging Reversible Adversarial Example (DP-RAE) generation framework, combining a heuristic black-box attack and RDH with Grayscale Invariance (RDH-GI) technology. This dual strategy not only evaluates and harnesses the adversarial potential of past perturbations more effectively but also guarantees flawless embedding of perturbation information and complete recovery of the original image. Experimental validation reveals our method's superiority, secured an impressive 96.9% success rate and 100% recovery rate in compromising black-box models. In particular, it achieved a 90% misdirection rate against commercial models under a constrained number of queries. This marks the first successful attempt at targeted black-box reversible adversarial attacks for commercial recognition models. This achievement highlights our framework's capability to enhance security measures without sacrificing attack performance. Moreover, our attack framework is flexible, allowing the interchangeable use of different attack and RDH modules to meet advanced technological requirements. © 2024 ACM.

关键词： Differential privacy

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ACSarF:a DRL-based adaptive consortium blockchain sharding framework for supply chain finance

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Digital Communications and networks 2025年第1期11卷 26-34页

作者： Shijing Hu Junxiong Lin Xin Du Wenbin Huang Zhihui Lu Qiang Duan Jie Wu School of Computer Science Fudan UniversityShanghai 200438China Shanghai Blockchain Engineering Research Center Fudan UniversityShanghai 200438China Information Sciences and Technology Department Pennsylvania State UniversityAbingtonPA 19001USA

Blockchain technologies have been used to facilitate Web 3.0 and FinTech ***,conventional blockchain technologies suffer from long transaction delays and low transaction success rates in some Web 3.0 and FinTech applications such as Supply Chain Finance(SCF).Blockchain sharding has been proposed to improve blockchain ***,the existing sharding methods either use a static sharding strategy,which lacks the adaptability for the dynamic SCF environment,or are designed for public chains,which are not applicable to consortium blockchain-based *** address these issues,we propose an adaptive consortium blockchain sharding framework named ACSarF,which is based on the deep reinforcement learning *** proposed framework can improve consortium blockchain sharding to effectively reduce transaction delay and adaptively adjust the sharding and blockout strategies to increase the transaction success rate in a dynamic SCF ***,we propose to use a consistent hash algorithm in the ACSarF framework to ensure transaction load balancing in the adaptive sharding system to further improve the performance of blockchain sharding in dynamic SCF *** evaluate the proposed framework,we conducted extensive experiments in a typical SCF *** obtained experimental results show that the ACSarF framework achieves a more than 60%improvement in user experience compared to other state-of-the-art blockchain systems.

关键词： Web 3.0 Consortium blockchain Blockchain sharding Supply chain finance FinTech regulation

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AN EMPIRICAL ANALYSIS OF FORGETTING IN PRE-TRAINED MODELS WITH INCREMENTAL LOW-RANK UPDATES 3

AN EMPIRICAL ANALYSIS OF FORGETTING IN PRE-TRAINED MODELS WI...

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3rd Conference on Lifelong Learning Agents, CoLLAs 2024

作者： Soutif-Cormerais, Albin Magistri, Simone van de Weijer, Joost Bagdanov, Andrew D. Computer Vision Center Spain Department of Computer Science Universitat Autònoma de Barcelona Spain Department of Information Engineering University of Florence Italy

Broad, open source availability of large pretrained foundation models on the internet through platforms such as HuggingFace has taken the world of practical deep learning by storm. A classical pipeline for neural network training now typically consists of finetuning these pretrained network on a small target dataset instead of training from scratch. In the case of large models this can be done even on modest hardware using a low rank training technique known as Low-Rank Adaptation (LoRA). While Low Rank training has already been studied in the continual learning setting, existing works often consider storing the learned adapter along with the existing model but rarely attempt to modify the weights of the pretrained model by merging the LoRA with the existing weights after finishing the training of each task. In this article we investigate this setting and study the impact of LoRA rank on the forgetting of the pretraining foundation task and on the plasticity and forgetting of subsequent ones. We observe that this rank has an important impact on forgetting of both the pretraining and downstream tasks. We also observe that vision transformers finetuned in that way exhibit a sort of "contextual" forgetting, a behaviour that we do not observe for residual networks and that we believe has not been observed yet in previous continual learning works. © 2024 CoLLAs. All Rights Reserved.

关键词： Open systems

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Semantic-Aware Pseudo-Labeling for Unsupervised Meta-Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第07期47卷 5475-5488页

作者： Ouyang, Tianran Dong, Xingping Ye, Mang Du, Bo Shao, Ling Shen, Jianbing Wuhan University School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China University of Chinese Academy of Sciences UCAS-Terminus AI Lab Beijing China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science China

In unsupervised meta-learning, the clustering-based pseudo-labeling approach is an attractive framework, since it is model-agnostic, allowing it to synergize with supervised algorithms to learn from unlabeled data. However, the pseudo-labels suffer from clustering noise and semantic chaos problems, further impacting the effectiveness of meta-learning. In this paper, we analyze and optimize the pseudo-labeling process, including encoding and clustering, aiming to generate semantic- like pseudo-labels to narrow the gap between unsupervised and supervised meta-learning. Firstly, during the encoding, we observe that the embedding space of existing methods lacks clustering-friendly properties, which is the primary reason for clustering noise. To address this issue, we minimize the inter-to-intra-class similarity ratio to generate clustering-friendly embedding features and validate our approach through comprehensive experiments. Then, during the clustering, we find that the semantic quality of pseudo-labels is not adequately controlled, resulting in semantic chaos of pseudo-labels. We propose a semantic-stability index to measure the semantic quality of pseudo-labels quantitatively. Based on this index, we propose the Semantic-aware Pseudo-label Reassignment mechanism to generate semantic- like pseudo-labels for all samples. Our approach is model-agnostic and can easily be integrated into existing supervised methods. To demonstrate its generalization ability, we integrate it into two representative algorithms: MAML and EP. The results on three main few-shot benchmarks clearly show that the proposed method achieves significant improvement compared to state-of-the-art models. Notably, our approach also outperforms the corresponding supervised method in three tasks. The source code will be made publicly available. ©1979-2012 IEEE.

关键词： Latent semantic analysis

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Enhancing Wireless Sensor network Data Collection through Aerial Unmanned Vehicles

Enhancing Wireless Sensor Network Data Collection through Ae...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Ali Alheeti, Khattab M. Najim, Ali Hamza Satar, Nurhizam Safie Mohd Aliesawi, Salah A. Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq Faculty of Engineering Department of Electrical and Computer Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (纸本)9798350369106

This study addresses the critical aspect of data collection within Wireless Sensor networks (WSNs), which consist of autonomous, compact sensor devices deployed to monitor environmental conditions. These networks have limited processing capacity, storage, short-range communication capabilities, and constrained battery power. A primary challenge in WSN s revolves around data gathering, particularly when employing static sinks, leading to premature energy depletion in nodes near these sinks. Integrating mobile collectors introduces the potential for energy conservation by shortening data routes. This article introduces three distinct algorithms for optimizing data collection in WSN s. Our approach involves an in-depth analysis of diverse strategies to mitigate packet collisions and facilitate the retransmission of lost packets. Our findings underscore the considerable impact that packet collisions can exert on the overall network performance during data gathering within wireless sensor networks. Through empirical exploration and analysis, we highlight the significance of these algorithms in enhancing the efficiency, energy conservation, and reliability of data collection processes within WSNs. This research sheds light on these algorithms' vital role in addressing the pressing challenges of data gathering and network performance optimization in wireless sensor networks. © 2024 IEEE.

关键词： Aircraft accidents

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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