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Ensemble Deep Learning Approaches in Health Care:A Review

Computers, Materials & Continua 2025年第3期82卷 3741-3771页

作者： Aziz Alotaibi Department of Computer Science College of Computing and Information TechnologyTaif UniversityTaif21974Saudi Arabia

Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of ***,both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/*** the growth in popularity of deep learning and ensemble learning algorithms,they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big *** deep learning has exhibited significant performance in enhancing learning generalization through the use of multiple deep learning *** ensemble deep learning has large quantities of training parameters,which results in time and space overheads,it performs much better than traditional ensemble *** deep learning has been successfully used in several areas,such as bioinformatics,finance,and health *** this paper,we review and investigate recent ensemble deep learning algorithms and techniques in health care domains,medical imaging,health care data analytics,genomics,diagnosis,disease prevention,and drug *** cover several widely used deep learning algorithms along with their architectures,including deep neural networks(DNNs),convolutional neural networks(CNNs),recurrent neural networks(RNNs),and generative adversarial networks(GANs).Common healthcare tasks,such as medical imaging,electronic health records,and genomics,are also ***,in this review,the challenges inherent in reducing the burden on the healthcare system are discussed and ***,future directions and opportunities for enhancing healthcare model performance are discussed.

关键词： Deep learning ensemble learning deep ensemble learning deep learning approaches for health care health care

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A Support Vector Machine(SVM)Model for Privacy Recommending Data Processing Model(PRDPM)in Internet of Vehicles

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Computers, Materials & Continua 2025年第1期82卷 389-406页

作者： Ali Alqarni Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy *** key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and *** privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user *** address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving *** model analyzes data based on user demands and interactions with service providers or neighboring *** aims to minimize privacy risks while ensuring service continuity and *** SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy *** results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.

关键词： Support vector machine big data IoV privacy-preserving

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PerFedKG: two-stage information-loop federated knowledge graph for personalized privacy-preserving recommendation systems

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science China(information sciences) 2025年第4期68卷 98-99页

作者： Fan WANG Xuyun ZHANG Weiming LIU Li LI Yuwen LIU Zhongyuan ZHANG Guanfeng LIU Shengye PANG Xiaolong XU Lianyong QI College of Computer Science and Technology Zhejiang University Department of Computing Macquarie University College of Computer Science and Technology China University of Petroleum (East China) School of Software Nanjing University of Information Science and Technology

Knowledge graphs(KGs) effectively mitigate data sparsity in recommendation systems(RSs) by providing valuable auxiliary information [1]. However, traditional centralized KG-based RSs increase the risk of user privacy *** learning(FL) enhances RS's privacy by enabling model training on decentralized data [2]. Although integrating KG and FL can address both data sparsity and privacy issues in RSs [3], several challenges persist. CH1,Each client's local model relies on a consistent global model from the server, limiting personalized deployment to endusers.

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Four-wave mixing Bragg scattering for small frequency shift from silicon coupled microrings

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Chinese Physics B 2025年第1期34卷 351-355页

作者： Chang Zhao Chao Wu Pingyu Zhu Yuxing Du Yan Wang Miaomiao Yu Kaikai Zhang Ping Xu Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China

Frequency conversion is pivotal in nonlinear optics and quantum optics for manipulating and translating light signals across different wavelength *** frequency conversion between two light beams with a small frequency interval is a central *** this work,we design a pair of coupled silicon microrings wherein coupled-induced modesplitting exists to achieve a small frequency shift by the process of four-wave mixing Bragg *** an example,the signal can be up or down converted to the idler which is 15.5 GHz spaced when two pumps align with another pair of split *** results unveil the potential of coupled microring resonators for small interval frequency conversion in a high-fidelity,all-optical,and signal processing quantum frequency interface.

关键词： four-wave mixing Bragg scattering silicon coupled microrings

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A Novel CAPTCHA Recognition System Based on Refined Visual Attention

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Computers, Materials & Continua 2025年第4期83卷 115-136页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Monir Abdullah Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha67714Saudi Arabia Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell Computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated ***-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this ***,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising *** our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this *** approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA *** the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition *** rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our *** results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.

关键词： Text-based CAPTCHA recognition refined visual attention web security computer vision

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Smart produce prediction system for reducing food waste and improving food security in Qatar

Smart produce prediction system for reducing food waste and ...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Bouallegue, Seifeddine Al-Naemi, Salem College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar

ISBN: (纸本)9781032803722

Qatar is a small country with limited land resources for agriculture and thus it is essential to ensure food security by utilizing available resources efficiently. PPS provides a way to achieve this by using data from a variety of sources such as weather, population, economic indicators, hospitality needs, and consumption patterns to forecast demand for different types of produce. In this paper, we will explore how PPS can be used to improve food security and reduce food waste in Qatar by identifying potential benefits of PPS in several key areas. First, we will discuss how PPS can be used to optimize inventory management for farmers and retailers by forecasting future demand and adjusting production accordingly. Second, we will examine how PPS can improve the efficiency of the supply chain by identifying potential bottlenecks and inefficiencies in advance. Third, we will explore how PPS can be used to identify seasonal trends in produce consumption and assist farmers in planning for planting and harvesting times more effectively. Overall, the PPS is a powerful tool that can help to optimize agricultural production and reduce food waste in Qatar by providing insights and predictions based on data analysis. The use of PPS can help to improve food security by ensuring that the right amount of produce is produced and available for consumption in a timely manner. © 2025 The Author(s).

关键词： Food security Food waste reduction Inventory management Produce prediction systems (PPS) Supply chain efficiency

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Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

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science China(information sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

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Blockchain-Enabled Edge computing Techniques for Advanced Video Surveillance in Autonomous Vehicles

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Computers, Materials & Continua 2025年第4期83卷 1239-1255页

作者： Mohammad Tabrez Quasim Khair Ul Nisa Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBishaP.O.Box 551Saudi Arabia

The blockchain-based audiovisual transmission systems were built to create a distributed and flexible smart transport system(STS).This system lets customers,video creators,and service providers directly connect with each ***-based STS devices need a lot of computer power to change different video feed quality and forms into different versions and structures that meet the needs of different *** the other hand,existing blockchains can’t support live streaming because they take too long to process and don’t have enough computer *** amounts of video data being sent and analyzed put too much stress on networks for vehicles.A video surveillance method is suggested in this paper to improve the performance of the blockchain system’s data and lower the latency across the multiple access edge computing(MEC)*** integration of MEC and blockchain for video surveillance in autonomous vehicles(IMEC-BVS)framework has been *** deal with this problem,the joint optimization problem is shown using the actor-critical asynchronous advantage(ACAA)method and deep reinforcement training as a Markov Choice Progression(MCP).Simulation results show that the suggested method quickly converges and improves the performance of MEC and blockchain when used together for video surveillance in self-driving cars compared to other methods.

关键词： Blockchain multiple access edge computing video surveillance autonomous vehicles

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An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique

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Computer Modeling in Engineering & sciences 2025年第1期142卷 835-867页

作者： Sumaia Mohamed Elhassan Saad Mohamed Darwish Saleh Mesbah Elkaffas Department of Information Technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandria21526Egypt College of Computing and Information Technology Arab Academy for ScienceTechnology and Maritime TransportAlexandriaP.O.Box 1029Egypt

Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic *** detection of lung tumors significantly increases the chances of successful treatment and ***,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung ***-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate ***,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor *** overcome these disadvantages,dual-model or multi-model approaches can be *** research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of *** automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung ***8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single *** is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to ***,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive *** combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applicati

关键词： Lung cancer detection dual-model deep learning technique data augmentation CNN YOLOv8

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A reliable energy-efficient routing algorithm for WBAN using centrality measure

A reliable energy-efficient routing algorithm for WBAN using...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Ameen, Shayan Ahmed, Ghufran Malik, Rauf Shams Siddiqui, Shahbaz Kariz Kamal, Muhammad Akhunzada, Adnan Karachi Pakistan College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar

ISBN: (纸本)9781032803722

WBAN (Wireless body area networks) have enabled continuous real-time monitoring of health professionals and patients. For example, monitoring blood pressure and other biological indicators can help predict heart attacks. Real-time applications require constant communication between source and destination sensor nodes. Developing real-time applications while maintaining the energy of the critical sensor nodes is difficult due to the dynamic network architecture of WBAN. To improve the network lifetime, throughput in terms of energy-efficient routing protocol in WBAN will play a vital role with the help of identifying the critical nodes in WBAN. In this study, we proposed an energy-efficient SDWBAN (Software define-Wireless body area network) routing algorithm with the help of identifying the critical nodes and comparing them with the existing solutions. To determine the essential nodes of WBAN, we used the centrality measure. © 2025 The Author(s).

关键词： Routing algorithms

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