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A Distributed Framework for Large-scale Protein-protein Interaction data Analysis and Prediction Using MapReduce

IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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Intelligent Grazing Strategy of Grassland Via Optimization of Multiple Phases

Intelligent Grazing Strategy of Grassland Via Optimization o...

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IEEE International Conference on Big data Analysis (ICBDA)

作者： Limin Liu Jing Wang Runxiu Zhang Guangwen Liu Zhiwei Xu College of Data Science and Application Inner Mongolia University of Technology Hohhot China Chinese Academy of Sciences Institute of Computing Technology Beijing China

In the process of grazing on grassland, the number of grazing needs to consider the income of pastoralists and the affordability of pastures to ensure the healthy development of the ecological environment while maximizing economic benefits. To better help pastoralists graze rationally, this paper proposes an intelligent selection mechanism for grazing on grassland strategies via optimization of multiple phases. From the perspective of mechanism analysis, based on relevant grassland data, the main factors affecting grazing strategies are analyzed, and a multistage optimized intelligent selection model of grazing strategies is established. Finally, an example of the selection of reasonable grazing strategies in the Xilin Gol Grassland under different precipitation levels is given. Experiments have proved that this method can effectively solve the maximum number of sheep grazing, determine appropriate grazing strategies, and provide a basis for sustainable grazing while considering the main factors affecting grazing.

关键词： Economics Analytical models Precipitation Chemistry Biological system modeling Soil moisture Simulated annealing

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DG-YOLOT: A Lightweight Density Guided YOLO-Transformer for Remote Sensing Object Detection

DG-YOLOT: A Lightweight Density Guided YOLO-Transformer for ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Nandini Saini Chiranjoy Chattopadhyay Debasis Das Computer Science & Engineering Indian Institute of Technology Jodhpur Rajasthan India School of Computing and Data Sciences FLAME University Pune Maharashtra India

Deep learning-based object detection methods in natural image datasets have demonstrated remarkable accuracy and lower error rates than those of humans. As a result, they have gained significant attention in the field of remote sensing imagery. However, direct transferablity of these methods in remote sensing images face challenges such as scale variations, complex object distributions, and arbitrary orientations. In order to address these challenges, we propose the transformer-based object detector named as DG-YOLOT where we use a guided self-attention mechanism with YOLOv5 to enhance the potentiality of training with minimum computation. Instead of employing uniform size patches like the conventional vision transformer, we leverage density map patches which facilitates the extraction of diverse contextual information related to objects within the image, enhancing the differentiation capability of our model. Through extensive experiments conducted on the DOTAv2.0 dataset, our proposed model has demonstrated superior performance with 57.91% mean Average Precision (mAP) compared to other state-of-the-art object detectors.

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Recommender Systems Based on Tensor Decomposition

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Computers, Materials & Continua 2021年第1期66卷 621-630页

作者： Zhoubao Sun Xiaodong Zhang Haoyuan Li Yan Xiao Haifeng Guo School of Information Engineering Nanjing Audit UniversityNanjing211815China School of Computing National University of Singapore117417Singapore Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of TechnologyNanjing211169China

Recommender system is an effective tool to solve the problems of information *** traditional recommender systems,especially the collaborative filtering ones,only consider the two factors of users and *** social networks contain abundant social information,such as tags,places and *** show that the social information has a great impact on recommendation *** not only describe the characteristics of items,but also reflect the interests and characteristics of *** the traditional recommender systems cannot parse multi-dimensional information,in this paper,a tensor decomposition model based on tag regularization is proposed which incorporates social information to benefit recommender *** original Singular Value Decomposition(SVD)model is optimized by mining the co-occurrence and mutual exclusion of tags,and their features are constrained by the relationship between *** on real dataset show that the proposed algorithm achieves superior performance to existing algorithms.

关键词： Recommender system social information tensor decomposition tag

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Type-Representation Enhanced Event Detection via Structure Encoder

Type-Representation Enhanced Event Detection via Structure E...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Yuming Feng Linmeng Huang Haozhe Liang Xiaolong Jin Saiping Guan Jiafeng Guo School of Computer Science and Technology University of Chinese Academy of Sciences Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Institute of Systems Engineering Academy of Military Science Beijing

Event detection, aiming to detect whether a sentence contains the type of events of interest, is one of the important tasks in information Extraction. There exist many methods for event detection, which usually treat event detection as a classification problem. These existing methods suffer from the imbalanced training data of different event types. To relieve this problem, this paper proposes a new Type-Representation-Enhanced Event Detection (TREED) method, which introduce hierarchical event type representations to enhance classification accuracy. Specifically, TREED first models the hierarchical relationship among event types in the dataset; Next, upon interactions between the representations of trigger words and event types, it obtains the new representations of trigger words integrating event type information. Finally, TREED classifies the events based on the obtained more accurate trigger word representations. Experimental results on a commonly used benchmark dataset, ACE-2005, demonstrate that the proposed model can improve both accuracy and recall of the event detection task.

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Prediction of Loan Approval in Banks Using Machine Learning Algorithm

Prediction of Loan Approval in Banks Using Machine Learning ...

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Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： Mathan Kumar Mounagurusamy P A Manoharan J. Anvar Shathik A. Pandiaraj N. Ramshankar Naveen G Department of Computing Technologies SRM Institute of Science and Technology Chennai Chennai Institute of Technology Chennai AIML and Data science Anjuman Institute of Technology and Management Karnataka India Department of Computer Science and Engineering R.M. D. Engineering College Department of Electronics and Communication Madha Institute of Engineering and Technology

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

Normally, the banking sector gains profit from different kinds of sources, but the main factor in their profitable growth is giving loans to their customers. A bank's profit or loss is determined by the quantity of the loan. A huge number of people are taking loans from banks and not clearing their dues. So there is a tedious process for banks to identify them and collect them. This in turn leads to following up on a complex verification process and issuing loans to their customers. Customer satisfaction is mandatory in every field, but in the banking sector, it is essential to identify genuine applicants and satisfy their needs, as well as to keep bankers free from non-payable customers. The manual method of identifying the right person and providing a loan to them may cause misconceptions. Several studies in this field suggest various kinds of approaches to identifying applicant status. Therefore, the use of machine learning (ML) approaches in the banking sector to resolve difficulties faced by bankers is becoming more popular. This reduces the problems faced by bankers and makes their applicants satisfactory. The time taken for this process is too long. It is essential to select the best ML approach from the different kinds of classifier algorithms available. The attributes chosen for this study include the applicant's education, type of work, applicant income, loan amount, co-applicant income, credit history, and property area. The proposed study reviews ML approaches such as K-nearest Neighboring (KNN), Logistic Regression, Decision Tree and Support vector Mechanism (SVM). Among those, KNN outperforms the other three algorithms, and the efficiency obtained by KNN is 87.50%, Logistic Regression is 83.512%, Decision Tree is 77.09%, and Random forest (RF) is 74.49%.

关键词： Support vector machines Logistic regression Machine learning algorithms Reviews Banking Nearest neighbor methods Vectors Sustainable development Recommender systems Testing

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U-Star: AN Asymmetric U-Shaped Network Based on Element-Wise Multiplication to Segment Nuclei in H&E Stained Histological Images 22

U-Star: AN Asymmetric U-Shaped Network Based on Element-Wise...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Yang, Guangzhengao Zhang, Li Zhao, Jie Chen, Zifan Li, Haoshen Dong, Bin Center for Data Science Peking University China Peking University National Engineering Laboratory for Big Data Analysis and Applications China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China National Biomedical Imaging Center Peking University China

ISBN: (纸本)9798331520526

Nuclei segmentation in Hematoxylin and Eosin (H&E) stained images plays a crucial role in cancer diagnosis and pathological evaluation, enabling pathologists to identify abnormal cells and assess their morphology and distribution. While current automated nuclei segmentation methods predominantly employ convolutional neural networks and attention mechanisms, the potential of element-wise multiplication has been largely unexplored. This paper introduces U-Star, a novel asymmetric segmentation network based on the star block that leverages element-wise multiplication. U-Star adopts the classic encoder-decoder architecture and innovatively implements star-connection as an alternative to traditional skip-connections. In experiments on an H&E stained image dataset, U-Star achieved superior performance with a Dice coefficient of 0.8783, accuracy of 0.9089, and IoU of 0.7929, significantly outperforming baseline models. Extensive ablation studies validate the effectiveness of the star-connection and demonstrate the advantages of our proposed framework. Beyond advancing the application of element-wise multiplication techniques, U-Star shows promising potential for broader applications in medical image segmentation. © 2025 IEEE.

关键词： Image segmentation

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Blockchain and Machine Learning Integration for Safe data Sharing in Finance and HR

Blockchain and Machine Learning Integration for Safe Data Sh...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Sivakumar, T.B. Prathiba, E. Sharma, Shweta Prasad, Rashmi Saket Ramaratnam, M.S. Krishnamoorthy, V. School of Computing Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India Mailam Engineering College Department of Artificial Intelligence and Data Science Mailam India Shambhunath Institute of Engineering & Technology Department of Business Administration Prayagraj India D Y Patil Pgdm Institute Akurdi Pune India Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya Department of Management Studies Kanchipuram India Kongu Engineering College Department of Management Studies Perundurai Erode India

ISBN: (纸本)9798350360660

Modern finance and HR businesses have challenges in exchanging data due to centralized systems that are subject to hacking and lack transparency. The study proposes employing blockchain technology and machine intelligence (ML) to address these issues holistically. The proposed system enhances integrity and resilience to assaults by utilizing a decentralized ledger for secure data exchange. ML algorithms enable predictive analytics, risk management, and personalized insights. The proposed system outperforms the existing system in terms of accuracy, security, and efficiency. The accuracy of fraud detection increases from 0.75 to 0.90, as does the estimate of staff turnover from 0.80 to 0.95. The proposed system achieves an overall accuracy of 95%, surpassing the existing system's 85%. Blockchain and ML integration can alter data management methods in finance and HR industries, with improved security measures resulting in a risk score of 2 compared to 7 in the existing system. © 2024 IEEE.

关键词： Decentralized finance

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Securing Consumer IoT Swarms Using Graph Transformers and SRAM for Firmware Attestation

Securing Consumer IoT Swarms Using Graph Transformers and SR...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Varun Kohli Bhavya Kohli Muhammad Naveed Aman Biplab Sikdar Department of Electrical and Computer Engineering National University of Singapore Singapore Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay India School of Computing University of Nebraska-Lincoln USA

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Consumer Internet of Things (IoT) networks have gained widespread popularity due to their convenience, automation, and security provisions in personal and home environments. Ubiquitous resource-constrained devices, however, are plagued with security issues that often arise from firmware-related issues and their propagated effects. While various studies on firmware attestation are available, they require firmware copies, specific hardware, and complex computation on the IoT device. This paper presents a study on the application of Graph Transformer Networks (GTN) in verifying the firmware integrity of consumer IoT swarms using SRAM as an attestation feature. The proposed method achieves an overall 0.99 accuracy on authentic samples from development and physical twin networks, 0.99 on malware, and 0.97 on propagated misbehavior at a $\sim 10^{-4}$ second inference latency on a laptop CPU.

关键词： Portable computers Accuracy Random access memory Transformers Malware Graph neural networks Hardware Internet of Things Security Microprogramming

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A Comprehensive Empirical Study of Query Performance Across GPU DBMSes

A Comprehensive Empirical Study of Query Performance Across ...

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作者： Suh, Young-Kyoon An, Junyoung Tak, Byungchul Na, Gap-Joo School of Computer Science and Engineering Department of Data Convergence Computing Kyungpook National University Korea Republic of School of Computer Science and Engineering Kyungpook National University Korea Republic of Artifcial Intelligence Research Laboratory Electronics and Telecommunications Research Institute Korea Republic of

In recent years, GPU database management systems (DBMSes) have rapidly become popular largely due to their remarkable acceleration capability obtained through extreme parallelism in query evaluations. However, there has been relatively little study on the characteristics of these GPU DBMSes for a better understanding of their query performance in various contexts. Also, little has been known about what the potential factors could be that affect the query processing jobs within the GPU DBMSes. To fill this gap, we have conducted a study to identify such factors and to propose a structural causal model, including key factors and their relationships, to explicate the variances of the query execution times on the GPU DBMSes. We have also established a set of hypotheses drawn from the model that explained the performance characteristics. To test the model, we have designed and run comprehensive experiments and conducted in-depth statistical analyses on the obtained empirical data. As a result, our model achieves about 77% amount of variance explained on the query time and indicates that reducing kernel time and data transfer time are the key factors to improve the query time. Also, our results show that the studied systems should resolve several concerns such as bounded processing within GPU memory, lack of rich query evaluation operators, limited scalability, and GPU under-utilization. © 2022 ACM.

关键词： Graphics processing unit

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