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Gastrointestinal Diseases Classification Using Deep Transfer Learning and Features Optimization

computers, Materials & Continua 2023年第4期75卷 2227-2245页

作者： Mousa Alhajlah Muhammad Nouman Noor Muhammad Nazir Awais Mahmood Imran Ashraf Tehmina Karamat College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Computer Engineering HITEC UniversityTaxila47080Pakistan Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan

Gastrointestinal diseases like ulcers, polyps’, and bleeding areincreasing rapidly in the world over the last decade. On average 0.7 millioncases are reported worldwide every year. The main cause of gastrointestinaldiseases is a Helicobacter Pylori (H. Pylori) bacterium that presents in morethan 50% of people around the globe. Many researchers have proposeddifferent methods for gastrointestinal disease using computer vision *** of them focused on the detection process and the rest of themperformed classification. The major challenges that they faced are the similarityof infected and healthy regions that misleads the correct classificationaccuracy. In this work, we proposed a technique based on Mask Recurrent-Convolutional Neural Network (R-CNN) and fine-tuned pre-trainedResNet-50 and ResNet-152 networks for feature extraction. Initially, the region ofinterest is detected using Mask R-CNN which is later utilized for the trainingof fine-tuned models through transfer learning. Features are extracted fromfine-tuned models that are later fused using a serial approach. Moreover, anImproved Ant Colony Optimization (ACO) algorithm has also opted for thebest feature selection from the fused feature vector. The best-selected featuresare finally classified using machine learning techniques. The experimentalprocess was conducted on the publicly available dataset and obtained animproved accuracy of 96.43%. In comparison with state-of-the-art techniques,it is observed that the proposed accuracy is improved.

关键词： Diseases deep learning endoscopy gastrointestinal tract transfer learning

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Application of optimized deformable convolution in underwater biological detection algorithm 3

Application of optimized deformable convolution in underwate...

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3rd International Symposium on computer Applications and Information Systems, ISCAIS 2024

作者： Zhuang, Hongwei Liu, Weisheng School of Computer and Software Engineering Liaoning University of Science and Technology Liaoning Anshan China

ISBN: (纸本)9781510681316

Underwater target detection is an important method for marine life detection. However, the accuracy of target detection and recognition is affected by the problems of image occlusion, blurred water quality and complex background of underwater targets. An improved YOLOV7 underwater biological target detection method is proposed to address the above problems. Firstly, the DCN structure is improved by incorporating the residual structure into the DCN, with new jump connections and 1×1 convolutional branches, so that the network can learn the residual information between the input and the output. It can improve the expressive ability of the model, reduce the number of parameters, and enhance the adaptability to the shape of the object. Finally, COT3 is integrated, which utilises a self-attention mechanism to weight the input feature maps so that the model can focus on important contextual information around the target and improve the model's ability to handle complex scenes. The experimental results show that the improved YOLOv7 model achieves an average detection accuracy mAP value of 80.4%, which is 2.8% higher than the original algorithm, and the algorithm is better than the original YOLOv7 algorithm in terms of detection accuracy and speed, which is more suitable for the detection of underwater targets. © 2024 SPIE.

关键词： Water quality

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Analysis and Classification of Fake News Using Sequential Pattern Mining

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Big Data Mining and Analytics 2024年第3期7卷 942-963页

作者： M.Zohaib Nawaz M.Saqib Nawaz Philippe Fournier-Viger Yulin He College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060 Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Department of Computer Science Faculty of Computing and Information TechnologyUnivesity of SargodhaSargodha 40100Pakistan

Disinformation,often known as fake news,is a major issue that has received a lot of attention *** researchers have proposed effective means of detecting and addressing *** machine and deep learning based methodologies for classification/detection of fake news are content-based,network(propagation)based,or multimodal methods that combine both textual and visual *** introduce here a framework,called FNACSPM,based on sequential pattern mining(SPM),for fake news analysis and *** this framework,six publicly available datasets,containing a diverse range of fake and real news,and their combination,are first transformed into a proper ***,algorithms for SPM are applied to the transformed datasets to extract frequent patterns(and rules)of words,phrases,or linguistic *** obtained patterns capture distinctive characteristics associated with fake or real news content,providing valuable insights into the underlying structures and commonalities of ***,the discovered frequent patterns are used as features for fake news *** framework is evaluated with eight classifiers,and their performance is assessed with various *** experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification,and that it expedites the classification task with high accuracy.

关键词： disinformation fake news sequential pattern mining(SPM) frequent patterns classification

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MCARNet: Mixed Convolution-Attention-Residual Network for Hyperspectral Image Classification of Cholangiocarcinoma 12

MCARNet: Mixed Convolution-Attention-Residual Network for Hy...

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12th International Conference on Computing and Pattern Recognition, ICCPR 2023

作者： Xue, Jiaqi He, Wangquan Ye, Songxin Liang, Hong Jia, Sen College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9798400707988

Hyperspectral is a well-established form of data in the medical field and plays an irreplaceable role, reflecting not only the size, shape and structure of the target pathological areas, but also the differences in spectral between the chemical composition of the targets. Therefore, hyperspectral image(HSI) can provide more abundant information for medical practitioners when performing pathology analysis. In recent years, while two powerful representational learning methods, which have been fully developed in the long term in deep learning, namely convolution and attention mechanism, have both made significant contributions to the pathological analysis of hyperspectral medical images. However, they have also revealed respective shortcomings in previous works, such as insufficient support of the biological basis of convolution and its weak ability to capture global information, the insufficient ability of attention mechanism to acquire spatial texture features, and both have over-reliance on sample quantity as well as quality. Therefore, this article proposes a Mixed Convolution-Attention-Residual(MCAR) model and a corresponding MCARNet network for the HSI classification task of cholangiocarcinoma by effectively combining multi-branch convolution, attention mechanism and residual structure. We also effectively validate that the proposed model can perform better in the task by parallel extraction of global with local features as well as the construction of residual structure under the condition of few training samples. © 2023 ACM.

关键词： Convolution

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Algorithm for Identifying Critical Nodes in Networks Based on Spectral Clustering to Construct Hypergraphs 16

Algorithm for Identifying Critical Nodes in Networks Based o...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Li, Xujie Shao, Fei Li, Haotian College of Computer Science and Software Engineering Hohai University Nanjing China College of Information Science and Engineering Hohai University Nanjing China

ISBN: (纸本)9798350390643

Currently, the basis for critical nodes definition and identification lies in the representation learning of the network and the extraction of local and global features of the nodes. The effectiveness of the algorithm is closely related to network modeling and network structural characteristics. However, with the increasing complexity of real-world systems, utilizing the continuity and integrity of traditional bipartite graph representation faces significant challenges. Different from traditional graph modeling, the hyperedges of a hypergraph can connect multiple nodes simultaneously, thus forming richer structural features and interaction information between nodes for the analysis and judgment of critical nodes. To more accurately identify critical nodes in complex networks, we propose an algorithm that establishes a hypergraph representation for traditional graph data to extract higher-order features of nodes for neural network training, in order to identify critical nodes in complex networks. The effectiveness of this method is proven by comparing it with various critical node identification algorithms under different infection rates. © 2024 IEEE.

关键词： Complex networks

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Dependable Service-Oriented Design of Healthcare IoT 7

Dependable Service-Oriented Design of Healthcare IoT

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7th IEEE International Conference on Smart Internet of Things, SmartIoT 2023

作者： Alsaig, Alaa Alagar, Vangalur Concordia University Computer Science and Software Engineering Montreal Canada

ISBN: (纸本)9798350316575

Healthcare Internet of Things (HIoT) is transforming healthcare industry by providing large scale connectivity for medical devices, patients, physicians, clinical staff, and nurses who may use them remotely to facilitate real-time monitoring based on the information gathered from the connected medical devices. This network provides both opportunity and challenges for collecting relevant healthcare information and sharing it for innovative research and efficient healthcare provision. Patient-centric information such as their health status and medical devices used by them must be protected to provide safety and offer privacy, while healthcare knowledge should be shared in confidence by experts for healthcare innovation and timely treatment of patients. In this paper a dependable service-oriented 3-tier architecture of HIoT is proposed. Context-aware role-based access control scheme is discussed to ensure services in HIoT are dependable, in the sense that every service provided to any patient in the network, anywhere and at anytime is safe, secure, reliable, and the privacy of patient receiving it is protected. © 2023 IEEE.

关键词： Internet of things

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Economic Analysis of Joint Mobile Edge Caching and Peer Content Sharing 42

Economic Analysis of Joint Mobile Edge Caching and Peer Cont...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Jiang, Changkun Shenzhen University College of Computer Science and Software Engineering China

ISBN: (纸本)9798350334142

Mobile edge caching (MEC) allows edge devices to cache popular contents and deliver them to end-users directly, which can effectively alleviate increasing backbone loads and improve end-users' quality of service. Peer content sharing (PCS) enables edge devices to share the cached contents with others, and can further increase the caching efficiency. While many efforts have been made to content caching or sharing technologies, it remains open to study the complicated technical and economic interplay between both technologies. In this paper, we propose a joint MEC-PCS framework, and focus on capturing the strategic interactions between different types of edge devices. Specifically, we model their interactions as a non-cooperative game, where each device (player) can choose to be an agent (who caches and shares contents with others) or a requester (who doesn't cache but requests contents from others) of each content. We analyze the existence and uniqueness of the game equilibrium systematically under the generic usage-based pricing (for PCS). To address the incomplete information issue, we further design a behavior rule that allows players to achieve the equilibrium via a dynamic learning algorithm. Simulations show that the joint MEC-PCS framework can reduce the total system cost by 60%, compared with the benchmark pure caching system without PCS. © 2023 IEEE.

关键词： Economic analysis

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Topology Discovery in Autonomic Networks 36

Topology Discovery in Autonomic Networks

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Ghaderi, Parsa Atwood, J. William Narayanan, Lata Concordia University Computer Science and Software Engineering Montreal Canada

ISBN: (纸本)9781665477161

Access to a topological map is important to network management. Autonomic Networks have been developed, in part, because they provide a convenient, secure platform for the development of operations, administration and management (OAM) applications, and because they make it possible to substantially reduce the need for human input in many network management situations. Using the definition of Autonomic Networks that has been developed by the Network Management Research Group of the IRTF, and standardized by the ANIMA Working Group of the IETF, we present two methods for discovering and maintaining topological information within an Autonomic Network domain. Our first method is highly distributed, and is based on clustering, while the second method is more centralized, and takes advantage of the already-defined procedure for securely initializing the Autonomic Network. We implemented both solutions on a testbed configured with three different topologies. We report the relative performance of the proposed methods. © 2023 IEEE.

关键词： Network management

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CHAmbi: A New Benchmark on Chinese Ambiguity Challenges for Large Language Models

CHAmbi: A New Benchmark on Chinese Ambiguity Challenges for ...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Zhang, Qin Cai, Sihan Zhao, Jiaxu Pechenizkiy, Mykola Fang, Meng College of Computer Science and Software Engineering Shenzhen University China Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Computer Science University of Liverpool United Kingdom

ISBN: (纸本)9798891761681

Ambiguity is an inherent feature of language, whose management is crucial for effective communication and collaboration. This is particularly true for Chinese, a language with extensive lexical-morphemic ambiguity. Despite the wide use of large language models (LLMs) in numerous domains and their growing proficiency in Chinese, there is a notable lack of datasets to thoroughly evaluate LLMs' ability to handle ambiguity in Chinese. To bridge this gap, we introduce the CHAmbi dataset, a specialized Chinese multi-label disambiguation dataset formatted in Natural Language Inference. It comprises 4,991 pairs of premises and hypotheses, including 824 examples featuring a wide range of ambiguities. In addition to the dataset, we develop a series of tests and conduct an extensive evaluation of pre-trained LLMs' proficiency in identifying and resolving ambiguity in the Chinese language. Our findings reveal that GPT-4 consistently delivers commendable performance across various evaluative measures, albeit with limitations in robustness. The performances of other LLMs, however, demonstrate variability in handling ambiguity-related tasks, underscoring the complexity of such tasks in the context of Chinese. The overall results highlight the challenge of ambiguity handling for current LLMs and underscore the imperative need for further enhancement in LLM capabilities for effective ambiguity resolution in the Chinese language. © 2024 Association for Computational Linguistics.

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Vehicle logo recognition based on depth residual shrinkage network 3

Vehicle logo recognition based on depth residual shrinkage n...

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3rd International Conference on Artificial Intelligence and computer engineering, ICAICE 2022

作者： Lin, Zhaobo Computer Science and Software Engineering Fuzhou University 350000 China

ISBN: (纸本)9781510663473

So as to reduce the interference of noisy information and enhance the accuracy of car marker image recognition in real life, a car marker image recognition model based on deep residual shrinkage network is proposed. The model combines attention mechanisms and soft thresholding function on the basis of deep residual network, which is used to eliminate noise and redundant information in the data, thus reducing the interference of noise information and improving the accuracy of image recognition. The experiments are conducted by adding Gaussian noise and pretzel noise to the car logo images shared by HFUT-VL on Github to simulate the car logo images captured under realistic conditions, forming a dataset of 200 images for each car logo with a total of 16000 images, and then training the deep residual shrinkage network with deep residual network and SENet algorithm model on the data. Then, the deep residual shrinkage network is compared with the deep residual network and SENet algorithm model to train the data, and some of the car mark images are tested. The results of the experiment show that the method has better performance than other deep neural network methods. © 2023 SPIE.

关键词： Shrinkage

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