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Enhancing Skin Cancer Diagnosis with Deep Learning:A Hybrid CNN-RNN Approach

computers, Materials & Continua 2024年第4期79卷 1497-1519页

作者： Syeda Shamaila Zareen Guangmin Sun Mahwish Kundi Syed Furqan Qadri Salman Qadri Faculty of Information Technology Beijing University of TechnologyBeijing100124China Computer Science International Engineering Collage Maynooth UniversityKildareW23 F2H6Irland Research Center for Data Hub and Security Zhejiang LabHangzhou311121China Computer Science Department MNS University of AgricultureMultan59220Pakistan

Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.

关键词： Skin cancer classification deep learning Convolutional Neural Network(CNN) RNN ResNet-50

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A distributed B+Tree indexing method for processing range queries over streaming data

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Cluster Computing 2024年第2期27卷 1251-1274页

作者： Safaee, Shahab Mirabi, Meghdad Rahmani, Amir Masoud Safaei, Ali Asghar Department of Computer Engineering Faculty of Engineering South Tehran Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan Department of Medical Informatics Faculty of Medical Sciences Tarbiat Modares University Tehran Iran

A data stream exhibits as a massive unbounded sequence of data elements continuously generated at a high rate. Stream databases raise new challenges for query processing due to both the streaming nature of data which constantly changes over time and the wider range of queries submitted by the user when compared with the traditional databases. In this paper, we propose a system architecture which includes components for both distributed indexing of streaming data and distributed processing of range queries on streaming data. Instead of creating a large and centralized B+Tree index structure, we create a set of small B+Tree indexes in such a way that a B+Tree index can be created for every partition of streaming data. We also design a distributed range search algorithm which can be used by each individual machine inside a Spark cluster to independently process range queries on each partition of streaming data. By exploiting the proposed system architecture, the process of indexing of streaming data and the process of querying over streaming data can be performed in a distributed and parallel manner. By performing several experiments, we demonstrate that our proposed indexing method is scalable and efficient for processing range queries on streaming data compared to the existing centralized B+Tree indexing methods and therefore, it can be used for applications involving data streams with a large volume of data elements and a large number of range queries. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Query processing

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Reversible Data Hiding in Shared Images with Separate Cover Image Reconstruction and Secret Extraction

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IEEE Transactions on Cloud Computing 2024年第1期12卷 186-199页

作者： Xiong, Lizhi Han, Xiao Yang, Ching-Nung Shi, Yun-Qing Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China National Dong Hwa University Department of Computer Science & Information Engineering Hualien974301 Taiwan New Jersey Institute of Technology ECE Department NewarkNJ07102 United States

Reversible data hiding is widely utilized for secure communication and copyright protection. Recently, to improve embedding capacity and visual quality of stego-images, some Partial Reversible Data Hiding (PRDH) schemes are proposed. But these schemes are over the plaintext domain. To protect the privacy of the cover image, Reversible Data Hiding in Encrypted Images (RDHEI) techniques are preferred. In addition, the full separability of cover image reconstruction and data restoration is also an important characteristic that cannot be achieved by most RDHEI schemes. To solve the issues, a partial and a complete Reversible Data Hiding in Shared Images with Separate Cover Image Reconstruction and Secret Extraction (RDHSI-SRE) are proposed in this paper. In the proposed schemes, the secret data is divided by Secret Sharing (SS). Then, the marked shared images are generated based on the proposed modify-and-recalculate strategy. The receiver can extract embedded data and reconstruct the image separably using k-out-of-n marked shared images. In the embedding phase of partial RDHSI-SRE (PRDHSI-SRE), the pixel values are modified according to the proposed Minimizing-Square-Errors Strategy to achieve high visual quality, and the complete RDHSI-SRE (CRDHSI-SRE) embeds data by modifying random coefficients to achieve reversibility. The experimental results and theoretical analyses demonstrate that the proposed schemes have a high embedding performance. Most importantly, the proposed schemes are fault-tolerant and completely separable. © 2013 IEEE.

关键词： Image reconstruction

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Imitation Learning from Purified Demonstrations 41

Imitation Learning from Purified Demonstrations

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Yunke Dong, Minjing Zhao, Yukun Du, Bo Xu, Chang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan Institute of Data Intelligence Wuhan University China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science Faculty of Engineering The University of Sydney Australia

Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning. While existing research has focused on optimizing with imperfect demonstrations, the training typically requires a certain proportion of optimal demonstrations to guarantee performance. To tackle these problems, we propose to purify the potential noises in imperfect demonstrations first, and subsequently conduct imitation learning from these purified demonstrations. Motivated by the success of diffusion model, we introduce a two-step purification via diffusion process. In the first step, we apply a forward diffusion process to smooth potential noises in imperfect demonstrations by introducing additional noise. Subsequently, a reverse generative process is utilized to recover the optimal demonstration from the diffused ones. We provide theoretical evidence supporting our approach, demonstrating that the distance between the purified and optimal demonstration can be bounded. Empirical results on MuJoCo and RoboSuite demonstrate the effectiveness of our method from different aspects. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Data Mining and Bioinformatics Approaches to Elucidate Bacterial Communities in the Extreme Environment

Data Mining and Bioinformatics Approaches to Elucidate Bacte...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Nirwantono, Rudi Asadi, Faisal Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Bacteria are microscopic organisms that can be found in many environments. They are abundant and have many roles in our life. Studying bacteria is essential so that we can identify the bacteria that are needed for many industrial applications. However, the main problem is that majority of the bacteria are unculturable, hampering the exploration of bacteria from different environments. Metagenomics approach which employs Next Generation Sequencing technology could help study bacteria by utilizing 16S rRNA marker gene. This study aims to demonstrate data mining and bioinformatics approaches to analyze 16S rRNA sequencing data. The raw sequencing data of 16S rRNA was collected from biological database. Then, the data were trimmed, denoised, and clustered to generate Amplicon Sequence Variants (ASVs). Diversity (alpha and beta) and taxonomic analyses were then conducted to elucidate the bacterial diversity and taxonomic profile of ASVs. The results of this work showed that Shannon and PD indices in China's hot spring were higher than Singapore and the USA. Furthermore, a significant difference was observed in the PD index. The unweighted UniFrac distance also showed there was a significant difference of the bacterial communities between the three locations. In addition, the taxonomic investigation unveiled prevalent bacterial groups in the ecosystem, namely Proteobacteria, Chloroflexi, Cyanobacteria, and Crenarchaeota. The research outcomes have the potential to serve as a foundational resource for subsequent bacterial metagenomic research, particularly the hot spring environment. © 2023 IEEE.

关键词： Bacteria

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Integrating multi-modal insights with transfer learning for detecting metastatic breast cancer (MBC-stage IV) prognostics

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International Journal of Information Technology (Singapore) 2025年第1期17卷 637-643页

作者： Kaur, Sugandha Kaur, Manpreet Khanna, Ashish Department of Computer Science Mata Sundri College for Women University of Delhi New Delhi India Department of Computer Science & Technology Manav Rachna University Faridabad India Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology New Delhi India Center for Global Health Research Saveetha Medical College Saveetha Institute of Medical and Technical Sciences Chennai India

Accurate breast cancer prognosis prediction is crucial for efficient treatment planning and improving patient outcomes. While significant progress has been made in treating primary breast cancer, the development of robust predictive models remains a critical challenge. This study introduces multi-modal prognostic approach for enhanced Metastatic Breast Cancer prediction. Recognizing the limitations of relying solely on uni-modal data, our approach leverages the power of multi-modal datasets. The images are taken from breast ultrasound scans dataset and mammograms are resized into 224 × 224, and finally, data pre-processing steps are used. This study presents a two-stage model: first, a convolutional neural network is used to extract salient features from the multi-modal data. These extracted features are further concatenated for feature-fusion and served as an input for a dual transfer learning hybrid model in the second stage by combining EfficientNetB4 and InceptionV3, thus enabling robust and accurate prognosis prediction. The outputs are flattened and concatenated followed by implementation of Deep_dense layers to learn the weights of the combined models. The proposed hybrid approach effectively categorizes medical images for the prognostics of breast-cancer. Evaluations demonstrate the superior performance of our proposed model, achieving an accuracy of 99.12%, precision of 95%, recall of 93%, and F1 score of 98.12%. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Convolutional neural network (CNN) EfficientNetB Feature fusion InceptionV3 Metastatic breast-cancer Multi-modal image data

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DMHFR:Decoder with Multi-Head Feature Receptors for Tract Image Segmentation

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computers, Materials & Continua 2025年第3期82卷 4841-4862页

作者： Jianuo Huang Bohan Lai Weiye Qiu Caixu Xu Jie He Department of Endoscopy Center Zhongshan Hospital(Xiamen)Fudan UniversityXiamen361015China School of Computing and Data Science Xiamen University MalaysiaSepang43900Malaysia School of Computer Science and Techonology Tongji UniversityShanghai200092China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou UniversityWuzhou543002China Xiamen Clinical Research Center for Cancer Therapy Xiamen361015China

The self-attention mechanism of Transformers,which captures long-range contextual information,has demonstrated significant potential in image ***,their ability to learn local,contextual relationships between pixels requires further *** methods face challenges in efficiently managing multi-scale fea-tures of different granularities from the encoder backbone,leaving room for improvement in their global representation and feature extraction *** address these challenges,we propose a novel Decoder with Multi-Head Feature Receptors(DMHFR),which receives multi-scale features from the encoder backbone and organizes them into three feature groups with different granularities:coarse,fine-grained,and full *** groups are subsequently processed by Multi-Head Feature Receptors(MHFRs)after feature capture and modeling *** include two Three-Head Feature Receptors(THFRs)and one Four-Head Feature Receptor(FHFR).Each group of features is passed through these MHFRs and then fed into axial transformers,which help the model capture long-range dependencies within the *** three MHFRs produce three distinct feature *** output from the FHFR serves as auxiliary auxiliary features in the prediction head,and the prediction output and their losses will eventually be *** results show that the Transformer using DMHFR outperforms 15 state of the arts(SOTA)methods on five public ***,it achieved significant improvements in mean DICE scores over the classic Parallel Reverse Attention Network(PraNet)method,with gains of 4.1%,2.2%,1.4%,8.9%,and 16.3%on the CVC-ClinicDB,Kvasir-SEG,CVC-T,CVC-ColonDB,and ETIS-LaribPolypDB datasets,respectively.

关键词： Medical image segmentation feature exploration feature aggregation deep learning multi-head feature receptor

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Optimal Location of Renewable Energy Generators in Transmission and Distribution System of Deregulated Power Sector:A Review

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Energy engineering 2025年第3期122卷 823-859页

作者： Digambar Singh Najat Elgeberi Mohammad Aljaidi Ramesh Kumar Rabia Emhamed Al Mamlook Manish Kumar Singla Department Electrical Engineering Government Polytechnic CollegeBTE LucknowEtah207120India University of Nevada RenoNV 89557USA Department of Computer Science Zarqa UniversityZarqa13110Jordan Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering&TechnologyChitkara UniversityChandigarh160002India Jadara University Research Center Jadara UniversityIrbid00962Jordan Department of Industrial Engineering Al Zawiya UniversityAl-ZawiyaP.O.Box 16418Libya Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Department of Biosciences Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India

The literature on multi-attribute optimization for renewable energy source(RES)placement in deregulated power markets is extensive and diverse in *** study focuses on the most relevant publications directly addressing the research problem at ***,while the body of work on optimal location and sizing of renewable energy generators(REGs)in balanced distribution systems is substantial,only the most pertinent sources are cited,aligning closely with the study’s objective function.A comprehensive literature review reveals several key research areas:RES integration,RES-related optimization techniques,strategic placement of wind and solar generation,and RES promotion in deregulated powermarkets,particularly within transmission ***,the optimal location and sizing of REGs in both balanced and unbalanced distribution systems have been extensively *** demonstrate significant potential for standalone applications in remote areas lacking conventional transmission and distribution *** presents a thorough review of current modeling and optimization approaches for RES-based distribution system location and ***,it examines the optimal positioning,sizing,and performance of hybrid and standalone renewable energy *** paper provides a comprehensive review of current modeling and optimization approaches for the location and sizing of Renewable Energy Sources(RESs)in distribution systems,focusing on both balanced and unbalanced networks.

关键词： Optimization of RESs distributed generation modeling and selection of RESs hybrid systems standalone systems optimal location

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MEC-IoT-Healthcare: Analysis and Prospects

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computers, Materials & Continua 2023年第6期75卷 6219-6250页

作者： Hongyuan Wang Mohammed Dauwed Imran Khan Nor Samsiah Sani Hasmila Amirah Omar Hirofumi Amano Samih M.Mostafa College of Computer Science Guangdong University of Science and TechnologyDongGuan523083China Department of Medical Instrumentation Techniques Engineering Dijlah University CollegeBaghdadIraq Department of Electrical Engineering University of Engineering&TechnologyPeshawar814Pakistan Center for Artificial Intelligence Technology Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia43600BangiSelangorMalaysia Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan Computer Science-Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt

Physical sensors,intelligent sensors,and output recommenda-tions are all examples of smart health technology that can be used to monitor patients’health and change their *** health is an Internet-of-Things(IoT)-aware network and sensing infrastructure that provides real-time,intelligent,and ubiquitous healthcare *** of the rapid development of cloud computing,as well as related technologies such as fog computing,smart health research is progressively moving in the right ***,fog computing,IoT sensors,blockchain,privacy and security,and other related technologies have been the focus of smart health research in recent *** the moment,the focus in cloud and smart health research is on how to use the cloud to solve the problem of enormous health data and enhance service performance,including cloud storage,retrieval,and calculation of health big *** article reviews state-of-the-art edge computing methods that has shifted to the collection,transmission,and calculation of health data,which includes various sensors and wearable devices used to collect health data,various wireless sensor technologies,and how to process health data and improve edge performance,among other ***,the typical smart health application cases,blockchain’s application in smart health,and related privacy and security issues were reviewed,as well as future difficulties and potential for smart health *** comparative analysis provides a reference for the the mobile edge computing in healthcare systems.

关键词： IoT mobile-edge computing cloud computing e-health

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S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning 41

S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning

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41st International Conference on Machine Learning, ICML 2024

作者： Wan, Guancheng Tian, Yijun Huang, Wenke Chawla, Nitesh V. Ye, Mang National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Department of Computer Science University of Notre Dame United States Taikang Center for Life and Medical Sciences Wuhan University Wuhan China

Graph Contrastive Learning (GCL) has emerged as a highly effective self-supervised approach in graph representation learning. However, prevailing GCL methods confront two primary challenges: 1) They predominantly operate under homophily assumptions, focusing on low-frequency signals in node features while neglecting heterophilic edges that connect nodes with dissimilar features. 2) Their reliance on neighborhood aggregation for inference leads to scalability challenges and hinders deployment in real-time applications. In this paper, we introduce S3GCL, an innovative framework designed to tackle these challenges. Inspired by spectral GNNs, we initially demonstrate the correlation between frequency and homophily levels. Then, we propose a novel cosine-parameterized Chebyshev polynomial as low/high-pass filters to generate biased graph views. To resolve the inference dilemma, we incorporate an MLP encoder and enhance its awareness of graph context by introducing structurally and semantically neighboring nodes as positive pairs in the spatial domain. Finally, we formulate a cross-pass GCL objective between full-pass MLP and biased-pass GNN filtered features, eliminating the need for augmentation. Extensive experiments on real-world tasks validate S3GCL proficiency in generalization to diverse homophily levels and its superior inference efficiency. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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