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Multi-Level Parallel Network for Brain Tumor Segmentation

Computer Modeling in engineering & Sciences 2024年第4期139卷 741-757页

作者： Juhong Tie Hui Peng School of Software Engineering Chengdu University of Information TechnologyChengdu610225China

Accurate automatic segmentation of gliomas in various sub-regions,including peritumoral edema,necrotic core,and enhancing and non-enhancing tumor core from 3D multimodal MRI images,is challenging because of its highly heterogeneous appearance and *** convolution neural networks(CNNs)have recently improved glioma segmentation ***,extensive down-sampling such as pooling or stridden convolution in CNNs significantly decreases the initial image resolution,resulting in the loss of accurate spatial and object parts information,especially information on the small sub-region tumors,affecting segmentation ***,this paper proposes a novel multi-level parallel network comprising three different level parallel subnetworks to fully use low-level,mid-level,and high-level information and improve the performance of brain tumor *** also introduce the Combo loss function to address input class imbalance and false positives and negatives imbalance in deep *** proposed method is trained and validated on the BraTS 2020 training and validation *** the validation dataset,ourmethod achieved a mean Dice score of 0.907,0.830,and 0.787 for the whole tumor,tumor core,and enhancing tumor core,*** with state-of-the-art methods,the multi-level parallel network has achieved competitive results on the validation dataset.

关键词： Convolution neural network brain tumor segmentation parallel network

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Deep learning-based open API recommendation for Mashup development

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Science China(information Sciences) 2023年第7期66卷 94-111页

作者： Ye WANG Junwu CHEN Qiao HUANG Xin XIA Bo JIANG School of Computer and Information Engineering Zhejiang Gongshang University Software Engineering Application Technology Lab

Mashup developers often need to find open application programming interfaces(APIs) for their composition application development. Although most enterprises and service organizations have encapsulated their businesses or resources online as open APIs, finding the right high-quality open APIs is not an easy task from a library with several open APIs. To solve this problem, this paper proposes a deep learning-based open API recommendation(DLOAR) approach. First, the hierarchical density-based spatial clustering of applications with a noise topic model is constructed to build topic models for Mashup clusters. Second,developers' requirement keywords are extracted by the Text Rank algorithm, and the language model is built. Third, a neural network-based three-level similarity calculation is performed to find the most relevant open APIs. Finally, we complement the relevant information of open APIs in the recommended list to help developers make better choices. We evaluate the DLOAR approach on a real dataset and compare it with commonly used open API recommendation approaches: term frequency-inverse document frequency, latent dirichlet allocation, Word2Vec, and Sentence-BERT. The results show that the DLOAR approach has better performance than the other approaches in terms of precision, recall, F1-measure, mean average precision,and mean reciprocal rank.

关键词： Mashup development open API recommendation deep learning neural network service discovery

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DeepCancer: deep learning for brain tumor detection-based application system

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Neural Computing and Applications 2025年第7期37卷 5577-5596页

作者： AlShowarah, Suleyman A. Department of Software Engineering Faculty of Information Technology Mutah University Al-Karak Jordan

A brain tumor is the abnormal cells that growth in the brain, and it is considered as one of the most dangerous diseases that lead to the cause of death. Diagnosis at early is important for increasing the survival rate from the brain tumors. Specialists can identify the tumors manually, but it is very time and effort consuming, and are subject to human error, especially when dealing with large amounts of images. The automatic identification algorithms-based applications can facilitate the process. This study aimed to investigate the possibility of detecting brain cancer based on images using Deep Learning (DL) techniques and statistical operations. The features were extracted using two models of Convolutional Neural Network (CNN), (VGG-19 and AlexNet), then they were used to generate new datasets for statistical operations. CNN is used to extract features with distinct details from brain MRI images. The data were trained in three different training–testing data splitting ratios. Then, the features were classified based on the KNN, RF, and SVM to find the best accuracy of brain MRI image. At the end, the obtained classification accuracy was in favor of statistical operations especially for Large-Value, and Merge between features using KNN (99.1) and SVM (99.1). The features that extracted used in this study can provide high influence on the classification accuracy. The results across all three training–testing data splitting ratios were almost similar, and this approves that the brain cancer can be identified with high accuracy even if the training dataset sizes were minimal. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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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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A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand

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Chinese Journal of Electronics 2024年第6期33卷 1383-1398页

作者： Xueqing YAN Yongming LI Sanjiang LI School of Computer Science Shaanxi Normal University School of Mathematics and Statistics Shaanxi Normal University Centre for Quantum Software and Information Faculty of Engineering and Information TechnologyUniversity of Technology Sydney

The tile-based multiplayer game Mahjong is widely played in Asia and has also become increasingly popular worldwide. Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tiles in turn until they complete a winning hand. An important notion in Mahjong is the deficiency number(*** number in Japanese Mahjong) of a hand, which estimates how many tile changes are necessary to complete the hand into a winning hand. The deficiency number plays an essential role in major decision-making tasks such as selecting a tile to discard. This paper proposes a fast algorithm for computing the deficiency number of a Mahjong hand. Compared with the baseline algorithm, the new algorithm is usually 100 times faster and, more importantly,respects the agent's knowledge about available tiles. The algorithm can be used as a basic procedure in all Mahjong variants by both rule-based and machine learning-based Mahjong AI.

关键词： Machine learning algorithms Knowledge based systems Decision making Asia Games

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A Cover-Independent Deep Image Hiding Method Based on Domain Attention Mechanism

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Computers, Materials & Continua 2024年第3期78卷 3001-3019页

作者： Nannan Wu Xianyi Chen James Msughter Adeke Junjie Zhao School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China

Recently,deep image-hiding techniques have attracted considerable attention in covert communication and high-capacity information ***,these approaches have some *** example,a cover image lacks self-adaptability,information leakage,or weak *** address these issues,this study proposes a universal and adaptable image-hiding ***,a domain attention mechanism is designed by combining the Atrous convolution,which makes better use of the relationship between the secret image domain and the cover image ***,to improve perceived human similarity,perceptual loss is incorporated into the training *** experimental results are promising,with the proposed method achieving an average pixel discrepancy(APD)of 1.83 and a peak signal-to-noise ratio(PSNR)value of 40.72 dB between the cover and stego images,indicative of its high-quality ***,the structural similarity index measure(SSIM)reaches 0.985 while the learned perceptual image patch similarity(LPIPS)remarkably registers at ***,self-testing and cross-experiments demonstrate the model’s adaptability and generalization in unknown hidden spaces,making it suitable for diverse computer vision tasks.

关键词： Deep image hiding attention mechanism privacy protection data security visual quality

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Design of multilayer cellular neural network based on memristor crossbar and its application to edge detection

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Journal of Systems engineering and Electronics 2023年第3期34卷 641-649页

作者： YU Yongbin TANG Haowen FENG Xiao WANG Xiangxiang HUANG Hang School of Information and software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China

Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application to edge *** MCM-CNN is designed by adopting a memristor crossbar composed of a pair of ***-CNN based on the memristor crossbar with changeable weight is suitable for edge detection of a binary image and a color image considering its characteristics of programmablization and *** of merit(FOM)is introduced to evaluate the proposed structure and several traditional edge detection operators for edge detection *** results show that the FOM of MCM-CNN is three times more than that of the traditional edge detection operators.

关键词： edge detection figure of merit(FOM) memristor crossbar synaptic circuit memristor crossbar-based cellular neural network(MCM-CNN)

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Multi-label, Classification-based Prediction of Breast Cancer Metastasis Directions

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第1期52卷 1-10页

作者： Wang, Tingting Fan, Qi Tan, Liang Zhang, Beier School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China

Predicting the metastatic direction of primary breast cancer (BC), thus assisting physicians in precise treatment, strict follow-up, and effectively improving the prognosis. The clinical data of 293,946 patients with primary BC diagnosed between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database. Multiple interpolations and Multi-label Synthetic Minority Over-sampling Technique methods were used for data analysis, and machine learning model was established for multi-label classification. Finally, Surgical information, lymph node status, distant metastasis, tumor size, chemotherapy, histological type, and radiotherapy had significant influence as inputs. Compared with the k-nearest neighbor model, average accuracies of the decision tree and random forest (RF) models increased from 88.84% to 93.59% and 94.14%, respectively. Their average precision, recall rate, F1 score, area under the receiver operating characteristic curve and weighted-F1 increased from 87.24% to 95.85% and 94.74%, 87.73% to 90.40% and 91.76%, 87.07% to 92.16% and 93.45%, 97.11% to 99.53% and 99.95%, 82.13% to 89.44% and 90.48%, respectively. In conclusion, the RF model, which showed the best performance, can be used in multi-label prediction of BC metastasis directions, and can assist physicians in diagnosing and treating patients with primary BC. © (2025), (International Association of Engineers). All rights reserved.

关键词： Lung cancer

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Research on Low Overhead Fast Repair Array Code Algorithm

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International Journal of Network Security 2025年第3期27卷 594-604页

作者： Xiong, Pan Hong, Tieyuan Wang, Yuheng Tang, Dan Cai, Hongliang Zhang, Junyan School of Software Engineering Chengdu University of Information Technology Sichuan Province Engineering Technology Research Center of Support Software of Informatization Application Chengdu610225 China

With the growth of exponential data, disk data storage is becoming increasingly important. In the storage system, single-disk and dual-disk loss are more and more serious. To ensure the efficiency and speed of data restoration is crucial to the reliability of the storage system. The Row Diagonal Parity Code (RDP) indicates that the combined recovery algorithm can reduce the total number of disks that are read when a single-disk fault is rectified by 25%. However, when a two-disk fault is rectified, all disk elements must work together. In order to improve the efficiency and speed of single and double disk fault repair, the RDP Code is extended, and a Rapid Rows Diagonal Parity Code (RRDP code) with local repair property is proposed. On the premise of global horizontal coding of RDP code, RRDP code is computed by XOR to generate a local horizontal check column, and it participates in global horizontal redundancy calculation. The local horizontal redundancy check column shortens the repair chain by constructing the"missing diagonal" and then it has the function of local repair. Through theoretical analysis, RRDP code has decent and fast decoding complexity and I/O reading cost of single and dual disk fault repair under the premise of constant coding cost, which greatly saves the fault repair cost. © (2025), (International Journal of Network Security). All rights reserved.

关键词： Restoration

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An Experimental Study on Dynamic Lifelong Learning with GPT for Mitigating Catastrophic Forgetting in Aspect-Based Sentiment Analysis

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IEEE Access 2025年 13卷 90316-90332页

作者： Huang, Huang Asemi, Adeleh Universiti Malaya Faculty of Computer Science Information Technology Department of Software Engineering Kuala Lumpur Malaysia

GPT is widely recognized as one of the most versatile and powerful large language models, excelling across diverse domains. However, its significant computational demands often render it economically unfeasible for individuals and small businesses, underscoring the need for efficient, domainspecific alternatives. In Aspect-Based Sentiment Analysis (ABSA), existing models are typically optimized for single domains, facing challenges in performing effectively across multiple domains. A key issue, known as "catastrophic forgetting," arises when models trained on one domain lose previously learned knowledge upon exposure to new domain data. This leads to two significant problems: limited cross-domain generalization and difficulty in retaining prior knowledge while learning domain-specific information. To address these challenges, we introduce the Dynamic Lifelong Learning Aspect-Based Sentiment Analysis GPT (DllaGPT), a model designed to handle multiple domains while mitigating catastrophic forgetting. Leveraging datasets from four ABSA domains—Laptops, Restaurants, Tweets, and Finance—this study fine-tunes a pretrained GPT model from HuggingFace sequentially across domains. DllaGPT employs a mechanism to retain real data from earlier domains during new domain training, effectively preserving prior knowledge. Experimental results highlight that DllaGPT achieves an average accuracy of 0.85 and a Backward Transfer (BWT) score of -0.09 across the four domains, showcasing its high accuracy and robust lifelong learning capabilities. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Contrastive Learning

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