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

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 High-Performance Hierarchical Snapshot Scheme for Hybrid Storage Systems

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Chinese Journal of Electronics 2018年第1期27卷 76-85页

作者： YU Xiao TAN Yu'an ZHANG Changyou LIANG Chen Khaled AOURRA ZHENG Jun ZHANG Quanxin School of Computer Science and technology Beijing Institute of Technology department of Computer Science and technology Shandong University of Technology Institute of software Chinese Academy of Science

With the increased snapshot operations(creation or deletion), the performance of storage system will degrade severely whether in Copy on write(COW)or Redirect on write(ROW). This paper presents a novel snapshot scheme which could use of storage resources effectively and keep the historical data accurately for SSDHDD-hybrid storage system. SSD(source volume) stores all the active data and executes read/write operations without responding to snapshot operations. In contrast,HDDs(snapshot volume) store the writing data sequentially in the form of logs and are responsible for backup and snapshot operations. In this way, the hybrid storage system can get the high throughput power via SSD even the snapshot functions are active and HDDs can keep pace with the high Input/Output operations per second(IOPS)capability of the SSD. Logs are combined and kept in a separate image volume by the controller. So, fine-grained Recovery point objective(RPO) and good Recovery time objective(RTO) can be achieved. Experiment results show that the prototype can achieve excellent I/O performance while snapshot operations are active and consume a few extra storage space.

关键词： Hybrid disk array Snapshot Copy on write(COW) Redirect on write(ROW) Recovery point objective(RPO) Recovery time objective(RTO)

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Induced neural stem cells regulate microglial activation through Akt-mediated upregulation of CXCR4 and Crry in a mouse model of closed head injury

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Neural Regeneration Research 2025年第5期20卷 1416-1430页

作者： Mou Gao Qin Dong Dan Zou Zhijun Yang Lili Guo Ruxiang Xu department of Neurosurgery Sichuan Academy of Medical Sciences and Sichuan Provincial People’s HospitalSchool of MedicineUniversity of Electronic Science and Technology of ChinaChengduSichuan ProvinceChina department of Neurosurgery Chinese PLA General HospitalBeijingChina department of Neurology Fu Xing HospitalCapital Medical UniversityBeijingChina

Microglial activation that occurs rapidly after closed head injury may play important and complex roles in neuroinflammation-associated neuronal damage and *** previously reported that induced neural stem cells can modulate the behavior of activated microglia via CXCL12/CXCR4 signaling,influencing their activation such that they can promote neurological ***,the mechanism of CXCR4 upregulation in induced neural stem cells remains *** this study,we found that nuclear factor-κB activation induced by closed head injury mouse serum in microglia promoted CXCL12 and tumor necrosis factor-αexpression but suppressed insulin-like growth factor-1 ***,recombinant complement receptor 2-conjugated Crry(CR2-Crry)reduced the effects of closed head injury mouse serum-induced nuclear factor-κB activation in microglia and the levels of activated microglia,CXCL12,and tumor necrosis factor-α.Additionally,we observed that,in response to stimulation(including stimulation by CXCL12 secreted by activated microglia),CXCR4 and Crry levels can be upregulated in induced neural stem cells via the interplay among CXCL12/CXCR4,Crry,and Akt signaling to modulate microglial *** agreement with these in vitro experimental results,we found that Akt activation enhanced the immunoregulatory effects of induced neural stem cell grafts on microglial activation,leading to the promotion of neurological recovery via insulin-like growth factor-1 secretion and the neuroprotective effects of induced neural stem cell grafts through CXCR4 and Crry upregulation in the injured cortices of closed head injury ***,these beneficial effects of Akt activation in induced neural stem cells were positively correlated with the therapeutic effects of induced neural stem cells on neuronal injury,cerebral edema,and neurological disorders post–closed head *** conclusion,our findings reveal that Akt activation may enhance the immunoregulatory effects of induced neura

关键词： Akt signaling cerebral edema closed head injury Crry CXCR4 induced neural stem cell microglia neuroinflammation

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Variable solution structure can be helpful in evolutionary optimization

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Science China(Information Sciences) 2015年第11期58卷 145-161页

作者： QIAN Chao YU Yang ZHOU Zhi-Hua National Key Laboratory for Novel software technology Nanjing University Collaborative Innovation Center of Novel software technology and Industrialization

Evolutionary algorithms are a family of powerful heuristic optimization algorithms where various representations have been used for solutions. Previous empirical studies have shown that for achieving a better efficiency of evolutionary optimization, it is often helpful to adopt rich representations(e.g., trees and graphs)rather than ordinary representations(e.g., binary coding). Such a recognition, however, has little theoretical justifications. In this paper, we present a running time analysis on genetic programming. In contrast to previous theoretical efforts focused on simple synthetic problems, we study two classical combinatorial problems, the maximum matching and the minimum spanning tree problems. Our theoretical analysis shows that evolving tree-structured solutions is much more efficient than evolving binary vector encoded solutions, which is also verified by experiments. The analysis discloses that variable solution structure might be helpful in evolutionary optimization when the solution complexity can be well controlled.

关键词： evolutionary algorithms genetic programming maximum matching minimum spanning tree running time computational complexity

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Detecting overlapping communities in networks via dominant label propagation

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Chinese Physics B 2015年第1期24卷 551-559页

作者：孙鹤立黄健斌田勇强宋擒豹刘怀亮 department of Computer Science and technology Xi’an Jiaotong University State Key Laboratory for Novel software technology Nanjing University School of software Xidian University School of Economics and Management Xidian University

Community detection is an important methodology for understanding the intrinsic structure and function of a realworld network. In this paper, we propose an effective and efficient algorithm, called Dominant Label Propagation Algorithm（Abbreviated as DLPA）, to detect communities in complex networks. The algorithm simulates a special voting process to detect overlapping and non-overlapping community structure in complex networks simultaneously. Our algorithm is very efficient, since its computational complexity is almost linear to the number of edges in the network. Experimental results on both real-world and synthetic networks show that our algorithm also possesses high accuracies on detecting community structure in networks.

关键词： overlapping community detection dominant label propagation complex network

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MARIE:One-Stage Object Detection Mechanism for Real-Time Identifying of Firearms

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 279-298页

作者： Diana Abi-Nader Hassan Harb Ali Jaber Ali Mansour Christophe Osswald Nour Mostafa Chamseddine Zaki Computer Science department Faculty of SciencesLebanese UniversityBeirut146404Lebanon College of Engineering and technology American University of the Middle EastEgaila54200Kuwait Lab-STICC CNRS UMR 6285ENSTA-BretagneBrest29200France

Security and safety remain paramount concerns for both governments and individuals *** today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to ***,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent *** advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying *** paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection *** SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction *** experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy *** these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.

关键词： Firearm and gun detection single shot multi-box detector deep learning one-stage detector MobileNet inception convolutional neural network

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An Intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV

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Chinese Journal of Electronics 2024年第4期33卷 910-919页

作者： Liang YAO Xiaolong XU Wanchun DOU Muhammad Bilal School of software Nanjing University of Information Science and Technology State Key Laboratory for Novel software technology Nanjing University department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies

As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive,and privacy-aware vehicular applications in Io V result in the transformation from cloud computing to edge computing,which enables tasks to be offloaded to edge nodes(ENs) closer to vehicles for efficient execution. In ITS environment,however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.

关键词： Industries Privacy Energy consumption Transportation Computational efficiency Encryption Protection

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Performance Prediction for Performance-Sensitive Queries Based on Algorithmic Complexity

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Tsinghua Science and technology 2013年第6期18卷 618-628页

作者： Chihung Chi Ye Zhou Xiaojun Ye School of software Tsinghua University department of Computer Science and technology Tsinghua University

Performance predictions for database queries allow service providers to determine what resources are needed to ensure their performance. Cost-based or rule-based approaches have been proposed to optimize database query execution plans. However, Virtual Machine （VM）-based database services have little or no sharing of resources or interactions between applications hosted on shared infrastructures. Neither providers nor users have the right combination of visibility/access/expertise to perform proper tuning and provisioning. This paper presents a performance prediction model for query execution time estimates based on the query complexity for various data sizes. The user query execution time is a combination of five basic operator complexities： O（1）, O（log（n））, O（n）, O（nlog（n））, and O（n2）. Moreover, tests indicate that not all queries are equally important for performance prediction. As such, this paper illustrates a performance-sensitive query locating process on three benchmarks： RUBiS, RUBBoS, and TPC-W. A key observation is that performance-sensitive queries are only a small proportion （20%） of the application query set. Evaluation of the performance model on the TPC-W benchmark shows that the query complexity in a real life scenario has an average prediction error rate of less than 10% which demonstrates the effectiveness of this predictive model.

关键词： query performance data size query complexity performance-sensitive query

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A fuzzy extension for SHOIQ based on comparisons between degrees of membership

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Science China(Information Sciences) 2013年第8期56卷 166-176页

作者： KANG DaZhou XU BaoWen LI YanHui department of Computing Science and technology Nanjing University Jiangsu Institute of software Quality

The current fuzzy extensions for description logics do not support representing and reasoning with complex fuzzy knowledge about comparisons between degrees of membership,which is very useful in practical *** investigate a novel approach of extending the description logic SHOIQ to SHOIQ FC by annotating concepts and roles based on comparison *** FC provides the ability to represent complex fuzzy knowledge,in addition to the expressive power of the fuzzy description logic *** main contributions are a decidability proof for the consistency problem of SHOIQ FC knowledge bases,as well as its tableau-based reasoning algorithm.

关键词： fuzzy description logic knowledge representation reasoning algorithm

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Heavy Traffic Limit Theorems for a Queue with Poisson ON/OFF Long-range Dependent Sources and General Service Time Distribution

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Acta Mathematicae Applicatae Sinica 2012年第4期28卷 807-822页

作者： Wan-yang DAI department of Mathematics and State Key Laboratory of Novel Software TechnologyNanjing University

In Internet environment, traffic flow to a link is typically modeled by superposition of ON/OFF based sources. During each ON-period for a particular source, packets arrive according to a Poisson process and packet sizes （hence service times） can be generally distributed. In this paper, we establish heavy traffic limit theorems to provide suitable approximations for the system under first-in first-out （FIFO） and work-conserving service discipline, which state that, when the lengths of both ON- and OFF-periods are lightly tailed, the sequences of the scaled queue length and workload processes converge weakly to short-range dependent reflecting Gaussian processes, and when the lengths of ON- and/or OFF-periods are heavily tailed with infinite variance, the sequences converge weakly to either reflecting fractional Brownian motions （FBMs） or certain type of long- range dependent reflecting Gaussian processes depending on the choice of scaling as the number of superposed sources tends to infinity. Moreover, the sequences exhibit a state space collapse-like property when the number of sources is large enough, which is a kind of extension of the well-known Little＇s law for M/M/1 queueing system. Theory to justify the approximations is based on appropriate heavy traffic conditions which essentially mean that the service rate closely approaches the arrival rate when the number of input sources tends to infinity.

关键词： reflecting fractional Brownian motion reflecting Gaussian process long-range dependence queueing process weak convergence

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