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Metaheuristic Based Clustering with Deep Learning Model for Big Data Classification

computer Systems science & engineering 2023年第1期44卷 391-406页

作者： R.Krishnaswamy Kamalraj Subramaniam V.Nandini K.Vijayalakshmi Seifedine Kadry Yunyoung Nam Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India Department of Biomedical Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatore641021India Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600077India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two *** the same time,clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in ***-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial *** data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce *** this motivation,this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics(MR-HDBCC).The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big *** addition,the MR-HDBCC technique involves three distinct processes namely pre-processing,clustering,and *** proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)techni-que which is capable of detecting random shapes and diverse clusters with noisy *** improving the performance of the DBSCAN technique,a hybrid model using cockroach swarm optimization(CSO)algorithm is developed for the exploration of the search space and determine the optimal parameters for density based ***,bidirectional gated recurrent neural network(BGRNN)is employed for the classification of big *** experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.

关键词： Big data data classification clustering mapreduce dbscan algorithm

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An Efficient Hybrid Quantum Variational Classifier With Matrix Product State

An Efficient Hybrid Quantum Variational Classifier With Matr...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Sun, Wanqi Xu, Jungang Duan, Chenghua School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

Matrix Product States have been extensively explored as a powerful tool for simulating quantum states in image classification task. However, most research has focused on classical simulations or computations involving high-dimensional unitaries, and significant challenges still exist in the practical preparation of Matrix Product States on quantum computers. This paper proposes a novel and practically feasible quantum variational algorithm based on Matrix Product States for image classification task. We design a hardware-efficient quantum circuit with several adjustable entangling operators to prepare the local tensors in Matrix Product States and integrate minimal residuals to ensure computational stability. We demonstrate that our algorithm can reduce the parameter complexity from growing exponentially with the system size to a linear scale. To validate the effectiveness of this quantum variational algorithm, we conducted experiments on the MNIST dataset, achieving accuracies of 99.95% and 95.96% for binary and ten-class classification tasks, which outperforms other related quantum algorithms. This work advances the practical application of quantum machine learning in resource-constrained environments of quantum computing. © 2025 IEEE.

关键词： Image classification Matrix product states Quantum circuit Variational quantum algorithm

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Evaluation of Vehicle Sensor Performance for Vulnerable User Detection through Vehicle Speed Measurement

Evaluation of Vehicle Sensor Performance for Vulnerable User...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Rathnakar, G. Kothiyal, Arnav Shwetha, H.R. Harikeerthan, M.K. Parikh, Swapnil Maranan, Ramya JSS Science and Technology University Department of Mechanical Engineering Karnataka 570006 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand 248 002 India JNN College of Engineering Department of Electronics and Communication Engineering Shivamogga577204 India Dayananda Sagar Academy of Technology and Management Department of Civil Engineering Karnataka 560082 India Parul Institute of Engineering and Technology Department of Computer Science and Engineering Parul University Gujarat India Saveetha School of Engineering Department of Research and Innovation SIMATS Tamil Nadu 602105 India

ISBN: (纸本)9798350385199

This work involves the successful utilization of magnetic sensors for vehicle speed measurement and also emphasizes the potential of such technology in enhancing safety for vulnerable road users. The device, with its wide voltage range compatibility (3.5-60 V) and robust industrial box housing, is equipped with a wireless module, enabling versatile deployment options. The vehicle detection system using MMC5883MA and HMC1021z sensors showed higher amplitudes for nearer lanes, suggesting a need to relocate the device further from the road. Challenges included close vehicle passes and inconsistent speeds, affecting accuracy, with an average error of 5.5 km / h, reduced to 3 km / h under ideal conditions. Despite fluctuations in some vehicles like the Citroen C3, 63% of measurements had deviations under 5 km / h, and 80% under 10 km / h. © 2024 IEEE.

关键词： Highway administration

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Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuclei Instance Segmentation in Histology Images

Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuc...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wu, Xuening Shen, Yiqing Zhao, Qing Kang, Yanlan Hu, Ruiqi Zhang, Wenqiang Fudan University Shanghai Engineering Research Center of Ai & Robotics Academy for Engineering & Technology Shanghai China Johns Hopkins University Department of Computer Science BaltimoreMD United States Hong Kong Polytechnic University Department of Applied Mathematics Hong Kong Fudan University Engineering Research Center of Ai & Robotics Ministry of Education Academy for Engineering & Technology School of Computer Science Shanghai China

ISBN: (纸本)9798350386226

Accurate nuclei segmentation is fundamental in histology image analysis, playing an essential role in cancer grading and diagnosis. However, this task remains challenging due to variations in staining protocols, heterogeneity among nuclei types, and the densely clustered nature of nuclei. While SAM exhibits zero-shot generalization capabilities in natural image segmentation, its performance degrades when applied to nuclei segmentation in histology images. Existing adaptations of SAM for medical imaging primarily focus on organ or lesion segmentation, which differs substantially from nuclei segmentation due to the unique characteristics of nuclei - specifically, their sparse distribution combined with dense clustering. To address these challenges, we propose Edge-SAN (Segment Any Nuclei with Edge Prompting), an interactive segmentation foundation model specifically designed for nuclei segmentation. Edge-SAN introduces a novel edge prompting method that enhances the delineation of nuclei boundaries, particularly among densely clustered nuclei, by leveraging edge information to improve segmentation accuracy. We evaluate Edge-SAN on 12 diverse datasets in both few-shot and zero-shot scenarios, demonstrating its effectiveness as a foundation model for nuclei segmentation, achieving 66.81% AJI and 73.13% DSC - improvements of 16.33% and 15.48% over SAM-Med2D, respectively. The code is available at https://***/deep-geo/Edge-SAN. © 2024 IEEE.

关键词： Deep Learning Digital Pathology Foundation Model Nuclei Segmentation Segment Anything Model

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Refining Pancreatic Cancer Detection and Classification through a Hybrid Approach: Stacked Deep Learning Integrated with the Sparrow Search Algorithm for Medical Image Analysis 2

Refining Pancreatic Cancer Detection and Classification thro...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Arunachalam, P. Devi, M. Kalpana Jose, Naduvathezhath Nessariose Vivekanandan, G. Sharma, Rinku Mahesh Yadav, Ganesh Kumar Department of Biomedical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology MCA Department Sreenivasa Institute of Technology and Management Studies Chittoor Architect Denken Solutions Department of CSE Sri Sairam Institute of Technology Chennai Department of Computer Science and Engineering SVKM Institute of technology Dhule Department of Computer Science and Engineering JSS Academy of Technical Education

ISBN: (纸本)9798350383867

In the endeavour to improve the precision and efficiency of diagnosing and classifying pancreatic cancer, there is a new idea that fuses deep learning and the Sparrow Search Algorithm with a stacked structure. That is why we consider this approach as revolutionary in terms of utilizing medical image analysis for the diagnosis in the field of pancreatic oncology with more accurate results. Transforming using stacked deep learning allows the systemto recursively learn its own complex feature in medical images, which is a basis for the identification of cancer. SBA or Sparrow Search Algorithm has been developed based on this social sparrow model and is a separate optimization technique which helps in optimizing the method of classification in this case. The foremost aim of this particular paper is to incorporate these two strategies and use the endproduct to improve the diagnostics of pancreatic cancer and also theclassification of the same. The cyclic process is applied together with the stacked deep learning process as well as the Sparrow Search Algorithm to make the systems' continuous optimization for sensitivity and specificity in detecting pancreatic malignancies. It is well to remember that this is not just a pure technological innovationthat holds promise of changing the landscape of pancreatic diagnosisand therapy. This approach of diagnosis and treatment also suggests early diagnosis, develop individual management plan and likely to yield higher survival rates for such patients. In addition, it is also worth considering at the potential that has been provided in enhancement of all sorts of branches of medicine linked to image analysis, along with the potential that has been developed when the best of technologies is paired with the best of ideas in computation. In conclusion, this paper captures the notion of intentionality and exigency in medical procedures. This union of stacked deep learningwith the Sparrow Search Algorithm unveils a new epoch in the medic

关键词： Deep learning

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Regressor Based Supermarket Sales Prediction Using Time Series Data 2

Regressor Based Supermarket Sales Prediction Using Time Seri...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Jeyarangani, J. Sumanth, Gorla Buchayyagari Raviram, Chakala Sreekanth, Mangala Venkata Srm Madurai College for Engineering and Technology Department of Infomration Technology Sivagangai India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India

ISBN: (纸本)9798350308846

In a variety of problem domains, including sales, finance, healthcare, the stock market, etc., forecasting techniques are applied. The supermarket sales forecast aids in increasing sales in a professional setting. The method aids in problem-domain decision-making. Most supermarket chains estimate and evaluate sales for the following year using, at most, ad hoc techniques and technologies. Many issues remain unresolved as a result of using traditional statistical methods to anticipate grocery sales. Supermarkets are in need of accurate sales forecasts. Most supermarket chain stores, use informal strategies and methodologies to analyze and forecast sales. This is due to a lack of knowledge about optimizing the prediction result. In order to solve this problem, this study creates the most accurate model for forecasting product sales at the supermarket based on training and testing results. Three models are created in this study to forecast product sales in a supermarket. These models, which forecast product sales in supermarkets across branches, are trained using historical supermarket data collected in diverse places. It investigates and compares the three machine learning models - Random Forest, Decision Tree, and Linear Regression for forecasting supermarket sales. This makes it easier for supermarkets to forecast product sales and helps them turn a profit. To identify the most accurate model, the prediction errors are computed. © 2023 IEEE.

关键词： Prediction regression algorithms Strategies and methodologies Supermarket sales

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A Hybrid Sentiment Analysis Model for Enhanced Stock Market Movement Predictions

A Hybrid Sentiment Analysis Model for Enhanced Stock Market ...

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Electrical engineering (ICEENG), International Conference on

作者： Karim Salaheldeen Amani A. Saad Mazen Nabil Elagamy Cherine Fathy Computer Engineering Department Arab Academy for Science and Technology (AAST) Alexandria Egypt Computer Engineering Department College of Engineering & Technology Arab Academy for Science and Technology (AAST) Egypt

ISBN: (数字)9798331519018

ISBN: (纸本)9798331519025

Nowadays, stock markets shape global economies and influence investment strategies and individuals' financial plans. Predicting stock trends has become increasingly important for all parties to deal with the complexities of these dynamically changing markets. Social media platforms, especially Twitter, have fundamentally changed market analysis by providing realtime data expressing public sentiment toward companies' performance. This paper leverages vast textual data available on Twitter to enhance stock trend prediction by extracting sentiment. To perform sentiment analysis, a hybrid sentiment analysis model is employed by integrating Financial Bidirectional Encoder Representations from Transformers (FinBert), Valence Aware Dictionary and Sentiment Reasoner (VADER), and KMeans clustering technique. The result of this model, combined with historical stock data, is fed as input to a voting ensemble classifier consisting of Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) to predict stock trends accurately. The proposed model, evaluated on the StockNet dataset, achieves superior performance compared to previous works on the same dataset, with a precision of 89.77% and an MCC of 0.81.

关键词： Sentiment analysis Analytical models Accuracy Social networking (online) Shape Blogs Predictive models Market research Stock markets Classification tree analysis

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RMIKD: An RDF and Metadata-Driven Scheme for Recommending Web Images for E-Commerce Fashion and Products Using Incremental Knowledge Derivation

RMIKD: An RDF and Metadata-Driven Scheme for Recommending We...

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International Conference on Data science, Computation and Security, IDSCS 2023

作者： Arulanantham Anujan, A. Deepak, Gerard Santhanavijayan, A. Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9789819709748

In this paper, the RMIKD framework, which is an RDF and metadata-driven framework for Web image recommendation, has been proposed that encompasses latency indexing and structural non-topic model for discovering and harnessing topics into the localized framework. Subsequently, the dataset is classified using the XGboost algorithm, which is a feature control machine learning but a strong framework that prevents deviation of the recommendation from the constituent domain and subsequently the framework generates RDF. The proposed framework generates RDF by using the RDF subject and object in order to end provision, strong label co-occurrence, turn semantics and generate ontologies to provide strong knowledge representation models in order to increase density of auxiliary knowledge into the framework. And most importantly, the framework also generates metadata and classifies it using transformers, which eases the overall handling of metadata and curates it as individual instances or capsules, and most importantly, a strong relevance computation scheme in terms of The Leacock Chodorow similarity, Normalized Compression Distance (NCD) and Sim Rank along with the Normalized Information Distance (NID) is being constituted at different parts or at different stages in the proposed RMIKD framework, which provides a strong ecosystem of computing semantic entity driven, semantic relatedness. The proposed RMIKD yields an overall average precision percentage of 97.07, average accuracy percentages of 97.88 and lowest FDR of 0.03. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Metadata

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Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2222-2232页

作者： Juanjuan Li Junqing Li Xiao Wang Rui Qin Yong Yuan Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the School of Artificial Intelligence Anhui UniversityHefei 266114 the Qingdao Academy of Intelligent Industries Qingdao 230031China the School of Mathematics Renmin University of ChinaBeijing 100872China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive *** security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing *** this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary ***,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and ***,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic *** incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading *** temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation ***,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

关键词： Auction data trading markets multi-blockchain pricing mechanisms

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