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A Multi-Layers Information Fused Deep Architecture for Skin Cancer Classification in Smart Healthcare

computers, Materials and Continua 2025年第3期83卷 5299-5321页

作者： Dillshad, Veena Khan, Muhammad Attique Nazir, Muhammad Ahmad, Jawad AlHammadi, Dina Abdulaziz Houda, Taha Cho, Hee-Chan Chang, Byoungchol Department of Computer Science HITEC University Taxila47080 Pakistan Department of Artificial Intelligence College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar31952 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Center for Computational Social Science Hanyang University Seoul01000 Korea Republic of Department of Computer Science Hanynag University Seoul01000 Korea Republic of

Globally, skin cancer is a prevalent form of malignancy, and its early and accurate diagnosis is critical for patient survival. Clinical evaluation of skin lesions is essential, but several challenges, such as long waiting times and subjective interpretations, make this task difficult. The recent advancement of deep learning in healthcare has shown much success in diagnosing and classifying skin cancer and has assisted dermatologists in clinics. Deep learning improves the speed and precision of skin cancer diagnosis, leading to earlier prediction and treatment. In this work, we proposed a novel deep architecture for skin cancer classification in innovative healthcare. The proposed framework performed data augmentation at the first step to resolve the imbalance issue in the selected dataset. The proposed architecture is based on two customized, innovative Convolutional neural network (CNN) models based on small depth and filter sizes. In the first model, four residual blocks are added in a squeezed fashion with a small filter size. In the second model, five residual blocks are added with smaller depth and more useful weight information of the lesion region. To make models more useful, we selected the hyperparameters through Bayesian Optimization, in which the learning rate is selected. After training the proposed models, deep features are extracted and fused using a novel information entropy-controlled Euclidean Distance technique. The final features are passed on to the classifiers, and classification results are obtained. Also, the proposed trained model is interpreted through LIME-based localization on the HAM10000 dataset. The experimental process of the proposed architecture is performed on two dermoscopic datasets, HAM10000 and ISIC2019. We obtained an improved accuracy of 90.8% and 99.3% on these datasets, respectively. Also, the proposed architecture returned 91.6% for the cancer localization. In conclusion, the proposed architecture accuracy is compared with

关键词： Patient treatment

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Novel Positive Multi-Layer Graph Based Method for Collaborative Filtering Recommender Systems

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Journal of computer science & Technology 2022年第4期37卷 975-990页

作者： Bushra Alhijawi Ghazi AL-Naymat Department of Computer Science Princess Sumaya University for TechnologyAmman 11941Jordan Artificial Intelligence Research Center College of Engineering and Information TechnologyAjman UniversityAjman 20550U.A.E.

Recommender systems play an increasingly important role in a wide variety of applications to help users find favorite products. Collaborative filtering has remarkable success in terms of accuracy and becomes one of the most popular recommendation methods. However, these methods have shown unpretentious performance in terms of novelty, diversity, and coverage. We propose a novel graph-based collaborative filtering method, namely Positive Multi-Layer Graph-Based Recommender System (PMLG-RS). PMLG-RS involves a positive multi-layer graph and a path search algorithm to generate recommendations. The positive multi-layer graph consists of two connected layers: the user and item layers. PMLG-RS requires developing a new path search method that finds the shortest path with the highest cost from a source node to every other node. A set of experiments are conducted to compare the PMLG-RS with well-known recommendation methods based on three benchmark datasets, MovieLens-100K, MovieLens-Last, and Film Trust. The results demonstrate the superiority of PMLG-RS and its high capability in making relevant, novel, and diverse recommendations for users.

关键词： recommender system collaborative filtering graph theory path search novelty coverage diversity

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CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models 62

CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous...

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62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Zhang, Tong Qin, Peixin Deng, Yang Huang, Chen Liang, Hongru Liu, Junhong Jin, Dingnan Lei, Wenqiang Chua, Tat-Seng College of Computer Science Sichuan University China Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education China National University of Singapore Singapore University of Electronic Science and Technology China

ISBN: (纸本)9798891760943

Large language models (LLMs) are increasingly used to meet user information needs, but their effectiveness in dealing with user queries that contain various types of ambiguity remains unknown, ultimately risking user trust and satisfaction. To this end, we introduce CLAMBER, a benchmark for evaluating LLMs using a well-organized taxonomy. Building upon the taxonomy, we construct ~ 12K high-quality data to assess the strengths, weaknesses, and potential risks of various off-the-shelf LLMs. Our findings indicate the limited practical utility of current LLMs in identifying and clarifying ambiguous user queries, even enhanced by chain-of-thought (CoT) and few-shot prompting. These techniques may result in overconfidence in LLMs and yield only marginal enhancements in identifying ambiguity. Furthermore, current LLMs fall short in generating high-quality clarifying questions due to a lack of conflict resolution and inaccurate utilization of inherent knowledge. In this paper, CLAMBER presents a guidance and promotes further research on proactive and trustworthy LLMs. Our dataset is available at https://***/SCUNLP/CLAMBER. © 2024 Association for Computational Linguistics.

关键词： Risk assessment

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Full-spectrum Bi@SrTiO_(3) for bi-directional promotion effects on photocatalytic redox reaction:Insight into intermediates and mechanism

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Journal of Rare Earths 2024年第5期42卷 917-929,I0004页

作者： Bin Liu Nanhao Zhou Jun Liu Xinglei Wang Peter K.J.Robertson Xiaoyuan Chen Chao Chen Jianmin Luo Chuanyi Wang School of Chemistry and Civil Engineering Shaoguan UniversityShaoguan512005China Chengdu Customs Technology Center Chengdu610041China Key Laboratory of Pollutant Chemistry and Environmental Treatment College of Chemistry and Environmental ScienceYili Normal UniversityYining835000China School of Chemistry and Chemical Engineering Queen's University BelfastStranmillis RoadBelfastBT95AGUnited Kingdom Guangzhou Institute of Industrial Intelligence(GIII) Guangzhou511400China

The development of full-spectrum photocatalysts active in the near-infrared(NIR)region has gained increasing attention for deleterious pollutant *** integration of plasmonic metals with semico nductors is an effective way to widen the light re sponse range of photocatalysts due to the strong light absorption and fast plasmonic energy transfer of the localized surface plasmon resonance(LSPR).In this work,the full-spectrum responsive Bi@SrTiO_(3) was prepared via facile solvothermal chemical *** optimal Bi@SrTiO_(3) achieves exceptional photocatalytic Cr(VI)reduction efficiency and tetracycline degradation,realizing bi-directional promotion effects on redox *** to density functional theory(DFT)simulations,the extraordinary photocatalytic performance is attributed to the tunable builtin electric field(IEF)of the Ohmic *** favorable adaptability in real water and high stability of BSTO-25 were proved by experime ntal *** the po ssible photocatalytic mechanism was proposed based on theoretical calculation and experimental ***,the non-toxicity of the BSTO-25 was evaluated by *** cultivation,which further proves the feasibility of treating wastewater with *** work provides a new perspective on constructing full-spectrum-driven photocatalysts for applications dealing with environmental remediation.

关键词： Full-spectrum photocatalysts Localized surface plasmon resonance Ohmic contact Cr(Ⅵ)reduction Antibiotic degradation Rare earths

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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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SpaCCC:Large Language Model-Based Cell-Cell Communication Inference for Spatially Resolved Transcriptomic Data

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Big Data Mining and Analytics 2024年第4期7卷 1129-1147页

作者： Boya Ji Xiaoqi Wang Debin Qiao Liwen Xu Shaoliang Peng College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China School of Computer Science Northwestern Polytechnical UniversityXi’an 710000China School of Computer and Artificial Intelligence and National Supercomputing Center in Zhengzhou Zhengzhou UniversityZhengzhou 450001China

Drawing parallels between linguistic constructs and cellular biology,Large Language Models(LLMs)have achieved success in diverse downstream applications for single-cell data ***,to date,it still lacks methods to take advantage of LLMs to infer Ligand-Receptor(LR)-mediated cell-cell communications for spatially resolved transcriptomic ***,we propose SpaCCC to facilitate the inference of spatially resolved cell-cell communications,which relies on our fine-tuned single-cell LLM and functional gene interaction network to embed ligand and receptor genes into a unified latent *** LR pairs with a significant closer distance in latent space are taken to be more likely to interact with each *** that,the molecular diffusion and permutation test strategies are respectively employed to calculate the communication strength and filter out communications with low *** benchmarked performance of SpaCCC is evaluated on real single-cell spatial transcriptomic datasets with superiority over other *** also infers known LR pairs concealed by existing aggregative methods and then identifies communication patterns for specific cell types and their signaling ***,SpaCCC provides various cell-cell communication visualization results at both single-cell and cell type *** summary,SpaCCC provides a sophisticated and practical tool allowing researchers to decipher spatially resolved cell-cell communications and related communication patterns and signaling pathways based on spatial transcriptome data.

关键词： Large Language Models(LLM) spatial transcriptome data Cell-Cell Communications(CCCs) functional gene interaction networks unified latent space

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Analysis of Knee Osteoarthritis Severity: A Deep Learning Paradigm for Automated Assessment from Plain Radiographs 8th

Analysis of Knee Osteoarthritis Severity: A Deep Learning Pa...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Panwar, Punita Chaurasia, Sandeep Gangrade, Jayesh Bilandi, Ashwani Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Artificial Intelligence and Data Science Jaipur Engineering College and Research Center Rajasthan Jaipur India Department of Orthopedic Mahatma Gandhi Medical College Rajasthan Jaipur India

ISBN: (纸本)9789819766741

Knee osteoarthritis (OA) is a chronic disease, also known as progressive joint disease which occurs due to continuous loss of cartilage. It generally affects the older person with age above 45 years. It is also known as "wear and tear" joint disease because it mostly affects the joints of humans like knee, hand, hips and spine. Osteoarthritis symptoms frequently grow over the time and result in worsening of articular cartilage. Pain, stiffness, tenderness, swelling, and bone spur are most common signs of osteoarthritis. Doctors normally use radiographic X-ray images for identification of OA. In medical field this is a highly time-consuming process;hence, to automate this process and rapidly diagnose, deep learning algorithms such as convolutional neural networks (CNN), are employed for the identification of knee osteoarthritis (OA) severity in X-ray scans, using the Kellgren-Lawrence (KL) grading scheme. The dataset utilized for this research was sourced from Kaggle, consisting of knee X-ray images. The dataset was partitioned into a training set including 5778 images, a testing set including 1661 images, and a validation dataset containing 826 images. The proposed study encompasses the quantification of grade-specific severity in knee osteoarthritis. The measured rates for each severity grade are G0 = 91%, G1 = 1%, G2 = 49.77%, G3 = 67.85%, and G4 = 88.46%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cartilage

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A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving

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Neural Computing and Applications 2025年第3期37卷 1651-1672页

作者： Xu, Junjie Chen, Yuzhong Xiao, Lingsheng Liao, Hongmiao Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Math word problem (MWP) represents a critical research area within reading comprehension, where accurate comprehension of math problem text is crucial for generating math expressions. However, current approaches still grapple with unresolved challenges in grasping the sensitivity of math problem text and delineating distinct roles across various clause types, and enhancing numerical representation. To address these challenges, this paper proposes a Numerical Magnitude Aware Multi-Channel Hierarchical Encoding Network (NMA-MHEA) for math expression generation. Firstly, NMA-MHEA implements a multi-channel hierarchical context encoding module to learn context representations at three different channels: intra-clause channel, inter-clause channel, and context-question interaction channel. NMA-MHEA constructs hierarchical constituent-dependency graphs for different levels of sentences and employs a Hierarchical Graph Attention Neural Network (HGAT) to learn syntactic and semantic information within these graphs at the intra-clause and inter-clause channels. NMA-MHEA then refines context clauses using question information at the context-question interaction channel. Secondly, NMA-MHEA designs a number encoding module to enhance the relative magnitude information among numerical values and type information of numerical values. Experimental results on two public benchmark datasets demonstrate that NMA-MHEA outperforms other state-of-the-art models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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Balancing Exploration–Exploitation of Multi-verse Optimizer for Parameter Extraction on Photovoltaic Models

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Journal of Bionic engineering 2024年第2期21卷 1022-1054页

作者： Yan Han Weibin Chen Ali Asghar Heidari Huiling Chen Xin Zhang College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehranIran School of Biomedical Engineering National Engineering Research Center of Ophthalmology and OptometryEye HospitalWenzhou Medical UniversityWenzhou325027China Key Lab of Biohealth Materials and Chemistry of Wenzhou Wenzhou UniversityWenzhouChina Key Laboratory of Intelligent Informatics for Safety&Emergency of Zhejiang Province Wenzhou UniversityWenzhouChina

Extracting photovoltaic(PV)model parameters based on the measured voltage and current information is crucial in the simulation and management of PV *** accurately and reliably extract the unknown parameters of different PV models,this paper proposes an improved multi-verse optimizer that integrates an iterative chaos map and the Nelder–Mead simplex method,*** experiments verified that the proposed INMVO fueled by both mechanisms has more affluent populations and a more reasonable balance between exploration and ***,to verify the feasibility and competitiveness of the proposal,this paper employed INMVO to extract the unknown parameters on single-diode,double-diode,three-diode,and PV module four well-known PV models,and the high-performance techniques are selected for *** addition,the Wilcoxon signed-rank and Friedman tests were employed to test the experimental results *** evaluation metrics,such as root means square error,relative error,absolute error,and statistical test,demonstrate that the proposed INMVO works effectively and accurately to extract the unknown parameters on different PV models compared to other *** addition,the capability of INMVO to stably and accurately extract unknown parameters was also verified on three commercial PV modules under different irradiance and *** conclusion,the proposal in this paper can be implemented as an advanced and reliable tool for extracting the unknown parameters of different PV *** that the source code of INMVO is available at https://***/woniuzuioupao/INMVO.

关键词： Photovoltaic models Multi-verse optimizer Nelder-Mead simplex Iterative chaos map

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Automatic mapping of gully from satellite images using asymmetric non-local LinkNet: A case study in Northeast China

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International Soil and Water Conservation Research 2024年第2期12卷 365-378页

作者： Panpan Zhu Hao Xu Ligang Zhou Peixin Yu Liqiang Zhang Suhong Liu College of Computer Sciences and Technology Chongqing University of Posts and TelecommunicationsChongqing Engineering Research Center for Spatial Big Data Intelligent TechnologyChongqing400065China College of Environment and Ecology Chongqing UniversityChongqing400044China Chongqing Municipal Ecological and Environmental Monitoring Center Chongqing401120China Beijing Key Laboratory of Environmental Remote Sensing and Digital Cities Faculty of Geographical ScienceBeijing Normal UniversityBeijing100875China China National Environmental Monitoring Centre Beijing100012China

Gully erosion can lead to the destruction of farmland and the reduction in crop *** mapping from remote sensing images is critical for quickly obtaining the distribution of gullies at regional scales and arranging corresponding prevention and control *** narrow and irregular shapes and similar colors to the surrounding farmland make mapping erosion gullies in sloping farmland from remote sensing images *** implement gully erosion mapping,we developed a small training samples-oriented lightweight deep leaning model,called asymmetric non-local LinkNet(ASNL-LinkNet).The ASNL-LinkNet integrates global context information through an asymmetric non-local operation and conducts multilayer feature fusion to improve the robustness of the extracted *** results show that the proposed ASNL-LinkNet achieves the best performance when compared with other deep learning *** quantitative evaluation results in the three test areas show that the F1-score of erosion gully recognition varies from 0.62 to *** study provides theoretical reference and practical guidance for monitoring erosion gullies on slope farmland in the black soil region of Northeast China.

关键词： Gully erosion mapping High-resolution satellite images Deep learning Black soil

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