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Deep Learning Enabled Intelligent Healthcare Management system in Smart Cities Environment

Computers, Materials & Continua 2023年第2期74卷 4483-4500页

作者： Hanan Abdullah Mengash Lubna A.Alharbi Saud S.Alotaibi Sarab AlMuhaideb Nadhem Nemri Mrim M.Alnfiai Radwa Marzouk Ahmed S.Salama Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk71491Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Science and Arts at MuhayelKing Khalid UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

In recent times,cities are getting smart and can be managed effectively through diverse architectures and *** cities have the ability to support smart medical systems that can infiltrate distinct events(i.e.,smart hospitals,smart homes,and community health centres)and scenarios(e.g.,rehabilitation,abnormal behavior monitoring,clinical decision-making,disease prevention and diagnosis postmarking surveillance and prescription recommendation).The integration of Artificial Intelligence(AI)with recent technologies,for instance medical screening gadgets,are significant enough to deliver maximum performance and improved management services to handle chronic *** latest developments in digital data collection,AI techniques can be employed for clinical decision making *** the other hand,Cardiovascular Disease(CVD)is one of the major illnesses that increase the mortality rate across the ***,wearables can be employed in healthcare systems that instigate the development of CVD detection and *** this motivation,the current study develops an Artificial Intelligence Enabled Decision Support system for CVD Disease Detection and Classification in e-healthcare environment,abbreviated as AIDSS-CDDC *** proposed AIDSS-CDDC model enables the Internet of Things(IoT)devices for healthcare data ***,the collected data is saved in cloud server for *** by,training 4484 CMC,2023,vol.74,no.2 and testing processes are executed to determine the patient’s health *** accomplish this,the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection(ISCO-FS)*** addition,Adam optimizer with Autoencoder Gated RecurrentUnit(AE-GRU)model is employed for detection and classification of *** experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

关键词： Smart cities e-healthcare decision support system cardiovascular disease deep learning feature selection

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An efficient memory data organization strategy for application-characteristic graph processing

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Frontiers of Computer science 2022年第1期16卷 191-193页

作者： Peng FANG Fang WANG Zhan SHI Dan FENG Qianxu YI Xianghao XU Yongxuan ZHANG Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage SystemEngineering Research Center of data storage systems and TechnologyMinistry of Education of ChinaSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan430074China

1 Introduction Graph processing has received significant attention for its ability to cope with large-scale and complex unstructured data in the ***,most of the graph processing applications exhibit an irregular memor... 详细信息

关键词： application processing pattern

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Flexible TiO_(2)-WO_(3−x) hybrid memristorwith enhanced linearity and synaptic plasticity for precise weight tuning in neuromorphic computing

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npj Flexible Electronics 2024年第1期8卷 180-191页

作者： Jianyong Pan Hao Kan Zhaorui Liu Song Gao Enxiu Wu Yang Li Chunwei Zhang Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinanChina State Key Lab of Transducer Technology Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghaiChina School of Integrated Circuits Shandong UniversityJinanChina

Tungsten oxide(WO_(3))-based memristors show promising applications in neuromorphic ***,single-layer WO_(3) memristors suffer from issues such as weak memory performance and nonlinear conductance *** this work,a functional layer based on the hybrids of WO_(3−x) and TiO_(2) is proposed for constructing flexible memristors featuring outstanding synaptic *** diverse electrical stimulations to the memristor enables a range of synaptic functions,elucidating its conduction mechanism through the conductive filament *** incorporation of TiO_(2) not only enhances the memristor’s memory characteristics but makes its conductance more linear,symmetrical and uniform during the long-term ***,in view of the enhanced device performance by TiO_(2) doping,the potential of this device for simple behavioral simulation and processing of complex computing problems is ***“learning-forgetting-relearning”characteristics and device integrability are visually *** the device to a convolutional neural network,the recognition accuracy of MNIST handwritten digits reaches 98.7%.

关键词： computing tuning precise

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Biologically Interpretable Model for Precise Recurrence Prediction of Non-Small Cell Lung Cancer 46

Biologically Interpretable Model for Precise Recurrence Pred...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Ai, Yang Du, Xiuju Liu, Jing Li, Yinhao Lin, Lanfen Li, Jingsong Chen, Yen-Wei Ritsumeikan University Graduate School of Information Science and Engineering Shiga Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Ritsumeikan University College of Information Science and Engineering Shiga Japan Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350371499

The precise prediction of preoperative recurrence in non-small cell lung cancer (NSCLC) that is suitable for clinical application is still an open question. Recent advancements integrating genomic data with deep learning have shown promise in enhancing recurrence analysis in NSCLC patients. However, the lack of interpretability in the decision-making process of DNN models has hindered their clinical trustworthiness. In this paper, we propose a novel Biologically Informed Pathway-Aware Neural Network (BioPAN). By automatically extracting biological prior knowledge to guide the architecture of DNN models, we design a unified architecture of gene-pathway-biological process-disease. This approach endows each neuron with entity meaning and learns a multi-level view of biological pathways and processes related to recurrence for fully interpretable NSCLC recurrence prediction. We demonstrated that the proposed model well explains the molecular mechanisms linking genes to NSCLC recurrence, identifies several genes that significantly promote and inhibit recurrence, and elucidates the pivotal roles of various gene pathways at the biological process level. Moreover, it outperforms classical machine learning methods, provides fully interpretable biological information, and requires fewer parameters. Broadly, BioPAN enables clinical discovery and prediction, and may have general applicability across cancer types. © 2024 IEEE.

关键词： Lung cancer

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Seastar: A Cache-Efficient and Load-Balanced Key-Value Store on Disaggregated Memory

Seastar: A Cache-Efficient and Load-Balanced Key-Value Store...

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2024 IEEE International Conference on Cluster Computing, CLUSTER 2024

作者： Du, Jingwen Wang, Fang Feng, Dan Zeng, Dexin Yi, Sheng Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System China School of Computer Science and Technology Huazhong University of Science and Technology China Shenzhen Huazhong University of Science and Technology Research Institute China

ISBN: (纸本)9798350358711

In modern datacenters, memory disaggregation un-packs monolithic servers to build independent network-connected compute and memory pools, greatly improving resource uti-lization. Existing memory-disaggregated key-value stores adopt ownership sharing or ownership partitioning for request scheduling and they all show poor performance and scalability. The former generates multiple cache misses and multiple network round trips due to poor cache locality, while the latter fails to fully use resources on all Compute Nodes (CNs) due to load imbalance. In this paper, we propose Seastar, a fast and scalable key-value store that achieves high cache locality as well as load balancing. To enable high concurrency and ensure linearizability, Seastar exploits a self-verification version chain to provide out-of-place atomic updates and ensure consistent reads. To improve cache locality, Seastar leverages a pair-based ownership assigning scheme to assign the processing permission of requests corre-sponding to each key to a CN pair through two independent hash functions. To ensure load balancing among CNs, Seastar proposes a decentralized power-of-two-choices routing scheme to route the requests to the less-loaded CNs in CN pairs for handling. Experimental results demonstrate that Seastar improves the throughput by up to 6.7x and significantly reduces the latency compared with state-of-the-art memory-disaggregated key-value stores. © 2024 IEEE.

关键词： Scalability

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Self-organizing Competitive Neural Network Based Adaptive Sparse Representation for Magnetotelluric Data Denoising 10

Self-organizing Competitive Neural Network Based Adaptive Sp...

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10th International Conference on Environmental and engineering Geophysics, ICEEG 2023

作者： Liu, Xiaoqiong Li, Guang Li, Jin Zhou, Xiaohui Gu, Xianjie Zhou, Cong Gong, Meng Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System East China University of Technology Nanchang330013 China College of Information Science and Engineering Hunan Normal University Changsha410081 China

The existing sparse decomposition denoising methods for magnetotelluric (MT) data need to set the iterative stop condition manually, which not only has a large workload and high difficulty, but also easily causes subjective bias. To this end, we propose a new adaptive sparse representation method for MT data denoising. First, the data to be processed is divided into high-quality segments and noisy segments by machine learning algorithm. Then, the characteristic parameters of high-quality segments are calculated, and the boundary value of the characteristic parameters is taken as the threshold. The threshold has two functions, one is as a criterion for signal-to-noise identification, and the other is as an iterative stop condition for subsequent sparse decomposition. Finally, the optimized orthogonal matching pursuit algorithm is used to separate the signal and noise of the noisy segments, and the denoised segments and high-quality segments are combined to obtain the complete denoised MT data. The field data processing results show that this method is a fully automatic and intelligent MT data denoising method. It greatly improves the signal-to-noise ratio and the apparent resistivity-phase curves. © Published under licence by IOP Publishing Ltd.

关键词： Signal to noise ratio

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Ethiopic Cryptography: An Efficient Hybrid Encryption Algorithm for Reducing Cybersecurity Attack 11th

Ethiopic Cryptography: An Efficient Hybrid Encryption Algori...

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11th EAI International Conference on Advancement of science and technology, ICAST 2023

作者： Akele, Tadesse Birara Bahiru, Tadesse Kebede Molla, Tibebu Legesse Fentie, Shambel Dessale Belay, Tadesse Destaw Marye, Almaz Yemataw Getaneh, Mersha Derese Department of Information System Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia Department of Information Technology Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia Deparment of Software Engineering Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia Department of Computer Science Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia

ISBN: (纸本)9783031641503

Cryptography is an essential tool in ensuring the security of data during network communication. Network and service providers prioritize data security to ensure secure data exchanges. Ethiopic cryptography is a unique encryption method that utilizes the Amharic language, prevalent in Ethiopia. Unlike Latin alphabets, Amharic consists of 287 characters, making decryption challenging. The encryption process involves converting the text to Amharic and then mapping it to a pair of randomly chosen two-bit English alphabets, creating an intermediate cipher. The Ethiopic cryptography algorithm employs a hybrid approach, using the RSA algorithm for secure key exchange and the Advanced Encryption Standard algorithm for text confidentiality. The algorithm comprises three phases—translation, mapping, and encryption—contributing to its effectiveness in securing data over the network. Decryption requires familiarity with the Amharic language and mapping details. The algorithm demonstrates a stronger avalanche effect (97%) in comparison with Blowfish and DES. The algorithm is proposed and evaluated using the design science research methodology, and its performance is validated through simulation using MATlab and BunnaScript tools, considering factors like the avalanche effect, time complexity, and memory consumption. The evaluation shows that the suggested solution operates faster, requires lesser time for data encryption, and uses less memory than other methods in use. Overall, the proposed Ethiopic cryptography algorithm proves to be an efficient and effective method for securing data during network communication, offering stronger text confidentiality and an improved avalanche effect. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： MATlab

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Large circuit models: opportunities and challenges

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science China(information sciences) 2024年第10期67卷 25-66页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y.YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah’s Ark Lab School of Integrated Circuits Peking University Faculty of Electrical Engineering and Computer Science Ningbo University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering The Chinese University of Hong Kong School of Integrated Circuits Southeast University Huawei HiSilicon Peng Cheng Laboratory School of Computer Science Peking University School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University School of Artificial Intelligence Shanghai Jiao Tong University

Within the electronic design automation(EDA) domain, artificial intelligence(AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding,overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level(RTL) designs, circuit netlists,and physical layouts. We champion the creation of large circuit models(LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area(PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

关键词： AI-rooted EDA large circuit models (LCMs) multimodal circuit representation learning circuit optimization

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TRNet: Two-Tier Recursion Network for Co-Salient Object Detection

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IEEE Transactions on Circuits and systems for Video technology 2025年第6期35卷 5844-5857页

作者： Cong, Runmin Yang, Ning Liu, Hongyu Zhang, Dingwen Huang, Qingming Kwong, Sam Zhang, Wei Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Northwestern Polytechnical University Brain and Artificial Intelligence Laboratory School of Automation Xi'an710129 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China Lingnan University Hong Kong

Co-salient object detection (CoSOD) is to find the salient and recurring objects from a series of relevant images, where modeling inter-image relationships plays a crucial role. Different from the commonly used direct learning structure that inputs all the intra-image features into some well-designed modules to represent the inter-image relationship, we resort to adopting a recursive structure for inter-image modeling, and propose a two-tier recursion network (TRNet) to achieve CoSOD in this paper. The two-tier recursive structure of the proposed TRNet is embodied in two stages of inter-image extraction and distribution. On the one hand, considering the task adaptability and inter-image correlation, we design an inter-image exploration with recursive reinforcement module to learn the local and global inter-image correspondences, guaranteeing the validity and discriminativeness of the information in the step-by-step propagation. On the other hand, we design a dynamic recursion distribution module to fully exploit the role of inter-image correspondences in a recursive structure, adaptively assigning common attributes to each individual image through an improved semi-dynamic convolution. Experimental results on five prevailing CoSOD benchmarks demonstrate that our TRNet outperforms other competitors in terms of various evaluation metrics. The code and results of our method are available at https://***/rmcong/TRNetTCSVT2025. © 1991-2012 IEEE.

关键词： HTTP

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Nanodiamond/Ti_(3)C_(2) MXene-coated quartz crystal microbalance humidity sensor with high sensitivity and high quality factor

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Rare Metals 2024年第6期43卷 2719-2729页

作者： Yao Yao Qiao Chen Yan-Qi Li Xian-He Huang Wei-Wei Ling Zhe-MiaoXie Jia-QiWang Chang-Ming Chen College of Communication Engineering Chengdu University of Information TechnologyChengdu 610225China School of Automation Chongqing University of Posts and TelecommunicationsChongqing 400065China School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Advanced Micro-/Nano-Devices Lab Department of Systems Design EngineeringUniversity of WaterlooWaterlooON N2L 3GlCanada

To address the challenge of achieving both high sensitivity and a high quality factor in quartz crystal microbalance(QCM)humidity sensors,a nanodiamond(ND)/Ti_(3)C_(2)MXene composite-coated QCM humidity sensor was *** material characteristics of ND,Ti_(3)C_(2)MXene,and ND/Ti_(3)C_(2)MXene composite were analyzed by transmission electron microscopy(TEM)and Fourier transform infrared(FTIR)*** experimental results demonstrated that the hydrophilic ND nanoparticles coated on Ti_(3)C_(2) MXene nanosheet prevented the self-stacking of Ti_(3)C_(2)MXene and enhanced the sensitivity of Ti_(3)C_(2) MXene-based QCM humidity ***,the high mechanical modulus of Ti_(3)C_(2) MXene material helped ND/Ti_(3)C_(2)MXene composite-coated QCM humidity sensor to achieve a high quality factor(>20,000).ND/Ti_(3)C_(2)MXene compositecoated QCM humidity sensor exhibited a sensitivity of 82.45 Hz/%RH,a humidity hysteresis of 1.1%RH,fast response/recovery times,acceptable repeatability,and good stability from 11.3%RH to 97.3%*** response mechanism of ND/Ti_(3)C_(2) MXene composite-coated QCM humidity sensor was analyzed in combination with a bi-exponential kinetic adsorption ***,the potential application of ND/Ti_(3)C_(2)MXene composite-coated QCM humidity sensor was demonstrated through its frequency response to wooden blocks with different moisture contents.

关键词： Humidity sensor Quartz crystal microbalance Nanodiamond Ti_(3)C_(2)MXene High quality factor

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