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3rd International Conference on Big-Data and Cloud Computing, ICBDCC 2019

作者： Mhathesh, T.S.R. Andrew, J. Martin Sagayam, K. Henesey, Lawrence Department of Bioinformatics Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Systems and Software Engineering School of Engineering Blekinge Institute of Technology Karlskrona Sweden

ISBN: (纸本)9789811552847

Identification and analysis of biological microscopy images need high focus and years of experience to master the art. The rise of deep neural networks enables analyst to achieve the desired results with reduced time and cost. Light sheet fluorescence microscopies are one of the types of 3D microcopy images. Processing microscopy images is tedious process as it consists of low-level features. It is necessary to use proper image processing techniques to extract the low-level features of the biological microscopy images. Deep neural networks (DNN) are efficient in extracting the features of images and able to classify with high accuracy. Convolutional neural networks (CNN) are one of the types of neural networks that can provide promising results with less error rates. The ability of CNN to extract the low-level features of images makes it popular for image classification. In this paper, a CNN-based 3D bacterial image classification is proposed. 3D images contain more in-depth features than 2D images. The proposed CNN model is trained on 3D light sheet fluorescence microscopy images of larval zebrafish. The proposed CNN model classifies the bacterial and non-bacterial images effectively. Intense experimental analyses are carried out to find the optimal complexity and to get better classification accuracy. The proposed model provides better results than human comprehension and other traditional machine learning approaches like random forest, support vector classifier, etc. The details of network architecture, regularization, and hyperparameter optimization techniques are also presented. © Springer Nature Singapore Pte Ltd 2021.

关键词： Image classification

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Improved convolutional neural multi-resolution wavelet network for COVID-19 pneumonia classification 4

Improved convolutional neural multi-resolution wavelet netwo...

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4th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2021

作者： Monday, Happy Nkanta Li, Jian Ping Nneji, Grace Ugochi Oluwasanmi, Ariyo Mgbejime, Goodness Temofe Ejiyi, Chukwuebuka Joseph Chikwendu, Ijeoma Amuche James, Edidiong Christopher School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9781665413220

Fast and early detection of infected patient is the most paramount step necessary to curb the spread of the COVID-19 disease. Radiographs have perhaps presented the fastest means of diagnosing COVID-19 in patients. The well-known standard for COVID-19 test requires a standard procedure and usually has low sensitivity. Previous studies have adopted various AI-based methods in detecting COVID-19 using both chest tomography and chest x-ray. In this study, the goal is to propose an enhanced convolutional neural multi-resolution wavelet network for COVID-19 pneumonia diagnosis. Our proposed model is a convolutional neural network integrated discrete wavelet transform of four level decomposition multiresolution analysis robust to handle few dataset which is very paramount due to the fast emergence of COVID-19. We evaluated our model based on three categories of public dataset of chest x-ray and chest tomography images. Our proposed model achieves 98.5% accuracy, 99.8% sensitivity, 98.2% specificity, and 99.6% AUC for multiple class categories with less training parameters. The results of this study show that our method achieves state-of-the-art result. © 2021 IEEE.

关键词： COVID-19

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HMGS: Hierarchical Matching Graph Neural Network for Session-Based Recommendation

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computers, Materials and Continua 2025年第3期83卷 5413-5428页

作者： Pengfei Zhang Rui Xin Xing Xu Yuzhen Wang Xiaodong Li Xiao Zhang Meina Song Zhonghong Ou State Grid Hebei Information and Telecommunication Branch Shijiazhuang 050000 China School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing 100876 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing 100876 China

Session-based recommendation systems (SBR) are pivotal in suggesting items by analyzing anonymized sequences of user interactions. Traditional methods, while competent, often fall short in two critical areas: they fail to address potential inter-session item transitions, which are behavioral dependencies that extend beyond individual session boundaries, and they rely on monolithic item aggregation to construct session representations. This approach does not capture the multi-scale and heterogeneous nature of user intent, leading to a decrease in modeling accuracy. To overcome these limitations, a novel approach called HMGS has been introduced. This system incorporates dual graph architectures to enhance the recommendation process. A global transition graph captures latent cross-session item dependencies, while a heterogeneous intra-session graph encodes multi-scale item embeddings through localized feature propagation. Additionally, a multi-tier graph matching mechanism aligns user preference signals across different granularities, significantly improving interest localization accuracy. Empirical validation on benchmark datasets (Tmall and Diginetica) confirms HMGS’s efficacy against state-of-the-art baselines. Quantitative analysis reveals performance gains of 20.54% and 12.63% in Precision@10 on Tmall and Diginetica, respectively. Consistent improvements are observed across auxiliary metrics, with MRR@10, Precision@20, and MRR@20 exhibiting enhancements between 4.00% and 21.36%, underscoring the framework’s robustness in multi-faceted recommendation scenarios.

关键词： Session-based recommendation graph network multi-level matching

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Intelligent Systems for Early Malaria Disease Detection in Patient Cells Using Transfer Learning Approaches

Intelligent Systems for Early Malaria Disease Detection in P...

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International Conference on Computing, Mathematics and engineering Technologies (iCoMET)

作者： Gul Zaman Khan Ibrar Ali Shah Farhatullah Muhammad Abul Hassan Hazrat Junaid Fouzia Sardar Department of Computer software Engineering UET Mardan Pakistan School of Automation China University of Geosciences Wuhan China Department of Information Engineering and Computer Science University of Trento Italy Department of Computer Science and Information Technology University of Malakand Pakistan Department of Zoology University f Malakand Dir Lower Pakistan

Malaria is an infectious disease spread by mosquitoes that effect humans and other animals. It is a massive threat to humanity, with instances growing each year. It is essential to prevent and diagnose malaria immediately and efficiently. For the time being, conventional methods are used for diagnosing malaria in which the patient's blood sample is examined by microscope or by using malaria RTD kits. This approach has several limitations because it requires medical expertise, is expensive, takes a long time, and the results are unsatisfactory. Artificial intelligence-based systems can prevent and help in diagnose of this infectious disease. Because of these limitations, the proposed work has proposed an AI-based diagnosis system that can detect malaria parasites immediately and efficiently. In the proposed experiment, we have applied four different pre-trained deep learning models on the image dataset with some preprocessing and optimization techniques for malaria parasite detection. After investigations, evaluation matrices such as precision, Recall, F1-score, sensitivity, and specificity are used to measure the performance of the proposed models. The Inception-Resnet outperformed by achieving 95% accuracy, VGG16 achieved 92% accuracy, inception achieved 93% accuracy, and VGG19 achieved 91% accuracy. The positive outcomes of this study show that this approach performs much better than the approaches currently used. Furthermore, the proposed method is relevant to health experts for screening purposes.

关键词： Deep learning Humanities Infectious diseases Microscopy Transfer learning Sensitivity and specificity Intelligent systems

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Joint Optimization of Dynamic Service Selection and Request Routing in Cloud-Edge Collaborative Environments

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IEEE Internet of Things Journal 2025年

作者： Tang, Bing Xu, Wei Zhang, Li Cao, Buqing Yang, Qing Li, Kuanching Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Guangzhou Maritime University School of Computer Science Guangzhou510725 China Huaihua University School of Computer and Artificial Intelligence Huaihua418000 China Hunan Institute of Engineering School of Information Science and Engineering Xiangtan411104 China Hunan University of Technology School of Computer Zhuzhou412007 China Hunan University of Science and Technology Sanya Research Institute Sanya572024 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China

Optimizing multi-instance service composition and dynamic request routing has become a critical challenge in cloud-edge collaborative systems. Existing solutions struggle with effectively balancing performance, cost, and bandwidth constraints in dynamic and resource-constrained environments. In this work, we address these challenges by proposing the Removed Minimum Cost Flow (RMCF) algorithm, aiming to minimize average response time while considering constraints such as budget and bandwidth, making it well-suited for time-sensitive services in a cloud-edge collaborative service provision system. Simulations were conducted using real-world data from China Telecom’s base stations in Shanghai, and experiments in various scenarios were considered, including time-sensitive services and general application services, with key performance indicators such as time utility, cost-utility, request completion rate, and timeout rate. The experimental results demonstrate that RMCF achieves lower response times, superior performance, and improved cost-effectiveness compared to other baseline algorithms. © 2014 IEEE.

关键词： Edge computing

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Hadoop-Oriented SVM-LRU (H-SVM-LRU): An Intelligent Cache Replacement Algorithm to Improve MapReduce Performance

arXiv

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arXiv 2023年

作者： Ghazali, Rana Adabi, Sahar Rezaee, Ali Down, Douglas G. Movaghar, Ali Department of Computer Engineering North Tehran Branch Islamic Azad University Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Computing and Software McMaster University 1280 Main St W HamiltonON Canada Department of Computer Engineering Sharif University of Technology Azadi Ave Tehran Iran

Modern applications can generate a large amount of data from different sources with high velocity, a combination that is difficult to store and process via traditional tools. Hadoop is one framework that is used for the parallel processing of a large amount of data in a distributed environment, however, various challenges can lead to poor performance. Two particular issues that can limit performance are the high access time for I/O operations and the recomputation of intermediate data. The combination of these two issues can result in resource wastage. In recent years, there have been attempts to overcome these problems by using caching mechanisms. Due to cache space limitations, it is crucial to use this space efficiently and avoid cache pollution (the cache contains data that is not used in the future). We propose Hadoop-oriented SVM-LRU (H-SVM-LRU) to improve Hadoop performance. For this purpose, we use an intelligent cache replacement algorithm, SVM-LRU, that combines the well-known LRU mechanism with a machine learning algorithm, SVM, to classify cached data into two groups based on their future usage. Experimental results show a significant decrease in execution time as a result of an increased cache hit ratio, leading to a positive impact on Hadoop performance. © 2023, CC BY-SA.

关键词： MapReduce

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EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection

EEiF: Efficient Isolated Forest with e Branches for Anomaly ...

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IEEE International Conference on Data Mining (ICDM)

作者： Yifan Zhang Haolong Xiang Xuyun Zhang Xiaolong Xu Wei Fan Qin Zhang Lianyong Qi School of software Nanjing University of Information Science and Technology China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China School of Computing Macquarie University Australia Uffield Department of Women's & Reproductive Health University of Oxford England College of Computer Science and Software Engineering Shenzhen University China Faculty of Science and Engineering University of Petroleum (East China) China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods.

关键词： Learning systems Training Accuracy Clustering algorithms Forestry Network security Fraud Time complexity Parallel algorithms Anomaly detection

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Toward Cost-effective Adaptive Random Testing: An Approximate Nearest Neighbor Approach

arXiv

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arXiv 2023年

作者： Huang, Rubing Cui, Chenhui Lian, Junlong Towey, Dave Sun, Weifeng Chen, Haibo The School of Computer Science and Engineering Macau University of Science and Technology Taipa999078 China The School of Computer Science and Communication Engineering Jiangsu University Jiangsu Zhenjiang212013 China The School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo315100 China The School of Big Data and Software Engineering Chongqing University Chongqing401331 China

Adaptive Random Testing (ART) enhances the testing effectiveness (including fault-detection capability) of Random Testing (RT) by increasing the diversity of the random test cases throughout the input domain. Many ART algorithms have been investigated such as Fixed-Size-Candidate-Set ART (FSCS) and Restricted Random Testing (RRT), and have been widely used in many practical applications. Despite its popularity, ART suffers from the problem of high computational costs during test-case generation, especially as the number of test cases increases. Although several strategies have been proposed to enhance the ART testing efficiency, such as the forgetting strategy and the k-dimensional tree strategy, these algorithms still face some challenges, including: (1) Although these algorithms can reduce the computation time, their execution costs are still very high, especially when the number of test cases is large;and (2) To achieve low computational costs, they may sacrifice some fault-detection capability. In this paper, we propose an approach based on Approximate Nearest Neighbors (ANNs), called Locality-Sensitive Hashing ART (LSH-ART). When calculating distances among different test inputs, LSH-ART identifies the approximate (not necessarily exact) nearest neighbors for candidates in an efficient way. LSH-ART attempts to balance ART testing effectiveness and efficiency. © 2023, CC BY.

关键词： Nearest neighbor search

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A Phase Estimation Algorithm for Quantum Speed-Up Multi-Party Computing

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computers, Materials & Continua 2021年第4期67卷 241-252页

作者： Wenbin Yu Hao Feng Yinsong Xu Na Yin Yadang Chen Zhi-Xin Yang Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Jiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering Michigan State UniversityEast Lansing48824MIUSA State Key Laboratory of Internet of Things for Smart City and Department of Electromechanical Engineering University of Macao999078Macao

Security and privacy issues have attracted the attention of researchers in the field of IoT as the information processing scale grows in sensor *** computing,theoretically known as an absolutely secure way to store and transmit information as well as a speed-up way to accelerate local or distributed classical algorithms that are hard to solve with polynomial complexity in computation or *** this paper,we focus on the phase estimation method that is crucial to the realization of a general multi-party computing model,which is able to be accelerated by quantum algorithms.A novel multi-party phase estimation algorithm and the related quantum circuit are proposed by using a distributed Oracle operator with *** proved theoretical communication complexity of this algorithm shows it can give the phase estimation before applying multi-party computing efficiently without increasing any additional ***,a practical problem of multi-party dating investigated shows it can make a successful estimation of the number of solution in advance with zero communication complexity by utilizing its special statistic *** simulations present the correctness,validity and efficiency of the proposed estimation method.

关键词： Edge computing security multi-party computing quantum algorithm phase estimation communication complexity

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Reasoning3D - Grounding and Reasoning in 3D: Fine-Grained Zero-Shot Open-Vocabulary 3D Reasoning Part Segmentation via Large Vision-Language Models

arXiv

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arXiv 2024年

作者： Chen, Tianrun Yu, Chunan Li, Jing Zhang, Jianqi Zhu, Lanyun Ji, Deyi Zhang, Yong Zang, Ying Li, Zejian Sun, Lingyun Tech. Co. Ltd China College of Computer Science and Technology Zhejiang University China School of Information Engineering Huzhou University China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore School of Software Technology Zhejiang University China School of Information Science and Technology University of Science and Technology of China China

In this paper, we introduce a new task: Zero-Shot 3D Reasoning Segmentation for parts searching and localization for objects, which is a new paradigm to 3D segmentation that transcends limitations for previous category-specific 3D semantic segmentation, 3D instance segmentation, and open-vocabulary 3D segmentation. We design a simple baseline method, Reasoning3D, with the capability to understand and execute complex commands for (fine-grained) segmenting specific parts for 3D meshes with contextual awareness and reasoned answers for interactive segmentation. Specifically, Reasoning3D leverages an off-the-shelf pre-trained 2D segmentation network, powered by Large Language Models (LLMs), to interpret user input queries in a zero-shot manner. Previous research have shown that extensive pre-training endows foundation models with prior world knowledge, enabling them to comprehend complex commands, a capability we can harness to "segment anything" in 3D with limited 3D datasets (source efficient). Experimentation reveals that our approach is generalizable and can effectively localize and highlight parts of 3D objects (in 3D mesh) based on implicit textual queries, including these articulated 3d objects and real-world scanned data. Our method can also generate natural language explanations corresponding to these 3D models and the decomposition. Moreover, our training-free approach allows rapid deployment and serves as a viable universal baseline for future research of part-level 3d (semantic) object understanding in various fields including robotics, object manipulation, part assembly, autonomous driving applications, augment reality and virtual reality (AR/VR), and medical applications. The code, the model weight, the deployment guide, and the evaluation protocol are: http://***/Reason3D/. © 2024, CC BY-NC-ND.

关键词： Semantics

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