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Cluster-preserving sampling algorithm for large-scale graphs

science China(Information sciences) 2023年第1期66卷 60-76页

作者： Jianpeng ZHANG Hongchang CHEN Dingjiu YU Yulong PEI Yingjun DENG National Digital Switching System E&T Research Center Information Engineering University Network Systems Department of the Strategic Support Force School of Computer Science and Technology Eindhoven University of Technology Center for Applied Mathematics Tianjin University

Graph sampling is a very effective method to deal with scalability issues when analyzing largescale graphs. Lots of sampling algorithms have been proposed, and sampling qualities have been quantified using explicit properties(e.g., degree distribution) of the sample. However, the existing sampling techniques are inadequate for the current sampling task: sampling the clustering structure, which is a crucial property of the current networks. In this paper, using different expansion strategies, two novel top-leader sampling methods(i.e., TLS-e and TLS-i) are proposed to obtain representative samples, and they are capable of effectively preserving the clustering structure. The rationale behind them is to select top-leader nodes of most clusters into the sample and then heuristically incorporate peripheral nodes into the sample using specific expansion strategies. Extensive experiments are conducted to investigate how well sampling techniques preserve the clustering structure of graphs. Our empirical results show that the proposed sampling algorithms can preserve the population's clustering structure well and provide feasible solutions to sample the clustering structure from large-scale graphs.

关键词： graph sampling clustering structure top-leader nodes expansion strategies large-scale graphs

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Image-based Plant Diseases Detection using Deep Learning

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Materials Today: Proceedings 2023年 80卷 3500-3506页

作者： Panchal, Adesh V. Patel, Subhash Chandra Bagyalakshmi, K. Kumar, Pankaj Khan, Ihtiram Raza Soni, Mukesh Department of Computer Science & Engineering GEC Patan India School of Computer Science and Engineering VIT Bhopal University Sehore MP Bhopal India Electrical and Electronics Engineering SRI Ranganathar Institute of Engineering and Technology Coimbatore India Department of Computer Science & Engineering Institute of Engineering & Technology Uttar Pradesh Greater Noida India Jamia Hamdard Computer Science Department Delhi India Jagran Lakecity University Bhopal India

Agriculture plays a major role in developing countries like India, however the food security still remains a vital issue. Most of the crops get wasted due to lack of storage facility, transportation, and plant diseases. More than 15% of the crops get wasted in India due to diseases and hence it has become one of the major concern to be resolved. There is a need of automatic system that can identify these diseases and help farmers to take appropriate steps to get rid of crop loss. Farmers have followed the conventional method of identifying the plant disease with their naked eyes, and it not possible for all the farmers to identify these diseases the same way. With the advance in Artificial Intelligence, there is a need to incorporate the facilities of the computer vision in the field of agriculture. Deep Learning rich libraries and user as well as developer friendly environment to work with, all these qualities make Deep Learning as the favorable method to get started with this problem. In this paper we have used Deep Learning because of the advantages it offers to work with images especially in image classification to get improvised results. The methodology includes taking leaves of infected crops and label them as per the disease pattern. The images of infected leaves are processed pixel based operations are applied to improve the information from the image. As a next step feature extraction is done followed by image segmentation and at the last classification of crop diseases based on the patterns extracted from the diseased leaves. The CNN (Convolutional Neural Network) is used for the classification of diseases, for the demonstration purpose the public dataset is used consisting of around 87 K images (RGB type images) including healthy as well as diseased leaves. © 2021

关键词： Crops

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Indian Supreme Court Judgement Dataset for Prediction Models

Indian Supreme Court Judgement Dataset for Prediction Models

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2023 Intelligent Computing and Control for engineering and Business systems, ICCEBS 2023

作者： Sivaranjani, N. Karthik, R. Sancith, Jaya Siva, P. Sri Sairam Engineering College Department of Computer Science and Business System India

ISBN: (纸本)9798350394580

The development of legal records requires extra time and their absurd length raises the need for programmed legal record handling frameworks. One of the handling steps is to recognize the essence of the reports expressions for legitimate case records. A computerized framework that could help a legal professional in anticipating the result of a case would help facilitate the legal interaction. For such a framework to be essentially valuable, expectations by the framework ought to be reasonable. To advance exploration in growing such a framework, this paper presents the Indian Legal Judgment Dataset (ILJD). ILJD is a huge corpus of 35k Indian Supreme Legal disputes commented on with unique court choices. A part of the corpus (a different test set) is commented on with best quality level clarifications by lawful specialists. © 2023 IEEE.

关键词： chi2 Legal Dataset Legal judgement Prediction Machine Learning Supreme Court of India

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Integrating ARIMA with Simple Moving Averages for Predictive Stock Price Modeling 1

Integrating ARIMA with Simple Moving Averages for Predictive...

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1st International Conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Kajendran, K. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

Forecasting stock prices is crucial because even minor incremental improvements translate into significant advantages in their financial decisions. A novel method that combines ARIMA Models with Simple Moving Averages (SMAs)is proposed to enhance prediction accuracy. SMAs for 5 and 10 days were adopted as exogenous variables in the ARIMA structure, achieving a phenomenal accuracy of 99 percent. The performance of the model was analyzed using statistical measures such as the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R2). The prediction is reaffirmed as valid by obtaining lower RMSE values while concurrently having high R2 scores. Graphical tools include the plot of the scatter plot, the plot of residuals, and the histogram of error distribution for examining model efficiency. This joint ARIMA-SMA modeling approach is highly effective and potentially a powerful tool for financial analysts and decision-makers: predicting stock prices with 99% accuracy might well represent a new horizon toward which researchers can steer predictive modeling in stock market analysis. © 2025 IEEE.

关键词： Prediction models

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Dual-State-Driven Evolutionary Optimization for Expensive Optimization Problems with Continuous and Categorical Variables 5

Dual-State-Driven Evolutionary Optimization for Expensive Op...

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5th International Conference on Data-Driven Optimization of Complex systems, DOCS 2023

作者： Xie, Lindong Li, Genghui Lin, Kangnian Wang, Zhenkun School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350393521

The surrogate-assisted evolutionary algorithm (SAEA) is one of the most efficient approaches for addressing expensive continuous or combinatorial optimization problems. However, it encounters significant challenges in expensive mixed-variable optimization problems (EMVOPs). To overcome this limitation, a dual-state-driven evolutionary optimization (called DSDEO), integrating a surrogate-assisted mixed-variable evolutionary optimization stage (MVEOS) and a surrogate-assisted continuous-variable evolutionary optimization stage (CVEOS), is proposed in this paper. Specifically, MVEOS employs global and local search to enhance the exploration and exploitation of the mixed-variable space. Global and local searches are alternately executed if one search fails to yield a better solution. CVEOS utilizes a continuous-improvement strategy to refine the continuous variables of the best solution obtained so far. Experimental results demonstrate the advantages of DSDEO compared to some state-of-the-art SAEAs on many benchmark problems. © 2023 IEEE.

关键词： Evolutionary algorithms

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Comprehensive Heart Attack Prediction Model Using Stacked Ensembles and Clinical Feature engineering

Comprehensive Heart Attack Prediction Model Using Stacked En...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Jayabalan, Anandhi Uma, R. Padmakala, S. Sri Sai Ram Engineering College Department of Computer Science Engineering Chennai India Saveetha School of Engineering Saveetha Institute Of Medical and Technical Sciences Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798331509828

Cardiovascular disease continues to be a predominant cause of mortality globally, requiring precise and effective strategies for early identification. This work examines the application of clinical datasets to explore ensemble models and machine learning for predicting heart attacks. The dataset includes patient information about factors such as age, diabetics, levels of cholesterol, blood pressure, and smoking status. The Label Encoder is utilized to encode categorical variables, while class imbalance is tackled through the application of the Synthetic Minority Over-sampling Technique (SMOTE). The top five features were identified through the application of feature importance in Random Forest analysis. To enhance prediction accuracy, various classification techniques, including Random Forest (RF), Gradient Boosting (GB), XG Boost, and Light GBM, along with an ensemble Voting Classifier, are utilized. The best model performance was attained by intensive hyperparameter optimization using Grid Search CV. The performance of the proposed system was assessed through a stacked ensemble methodology. The Random Forest model revealed an accuracy of 87.25% and a ROC-AVC of 0.91. The results for Gradient Boosting and XG Boost showed accuracies of 85.60% and 86.80%, accompanied by ROC-AUC scores of 0.89 and 0.90, respectively. The ensemble Voting Classifier enhanced performance, reaching an accuracy of 88.15% and a ROC-AUC of 0.92. The findings indicate that utilizing an ensemble method alongside hyperparameter optimization significantly enhances prediction accuracy and facilitates the early identification of heart attack risks. © 2025 IEEE.

关键词： Blood pressure

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Leveraging Random Forests for Stock Market Bubble Identification: A Comparative Analysis 1

Leveraging Random Forests for Stock Market Bubble Identifica...

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1st International Conference on Intelligent systems and Computational Networks, ICISCN 2025

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

This research attempts to identify stock market bubbles using technical indicators combined with machine-learning processes. If not detected early on, stock bubbles arising from overvaluation and speculation can incur huge losses. The study used Technical Indicators such as the Relative Strength Index(RSI), Moving Average Convergence Divergence(MACD), Bollinger Bands, and Volume Indicators to catch the market trends. These features were analyzed using Support Vector Machine, Logistic Regression, and Random Forest models with a 99.84% test accuracy and AUC equal to 1.0 obtained with Random Forest, which proved to be, in this research, the most reliable for bubble detection. The study compares, in detail, the results, including Confusion Matrices and Receiver Operating Characteristic curves, validating the findings. The approach here would be a robust and data-driven framework for investors and analysts to recognize bubbles, facilitating better decision-making by risk minimization. The results also contribute to further developments in the role of machine learning in equity markets. © 2025 IEEE.

关键词： Marketplaces

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Predicting Stock Prices with K-Nearest Neighbors using Moving Average Techniques 1

Predicting Stock Prices with K-Nearest Neighbors using Movin...

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1st International Conference on Intelligent systems and Computational Networks, ICISCN 2025

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

This research analyzes the effectiveness of the K-Nearest Neighbors algorithm combined with moving average techniques, Five-day and Ten-day Exponential Moving Averages, specifically EMA-5 and EMA-10, to predict stock prices. The study will focus on the adjusted closing price to deal with the autocorrelation issue that is common in time series data. The performance of the models was evaluated based on four key metrics: mean absolute error, mean squared error, root mean square error, coefficient of determination, overfitting risk test and generalization capability test. It was shown from the results that KNN models integrated with EMA-5 and EMA-10 offered high accuracy, one of which, with an R2 coefficient indicating the capacity to capture stock price trends with an accuracy of up to 99%. The research pointed out how short-term EMAs, combined with machine-learning algorithms, can substantially improve stock price prediction accuracy. It gives useful insights into stock price behaviour, therefore providing a decisive precedent for future works in creating a viable method for selecting trading strategies using machine-learning tools and moving averages. © 2025 IEEE.

关键词： Time series

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Spark workflow task scheduling with deadline and privacy constraints in hybrid cloud networks

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Soft Computing 2025年第2期29卷 783-801页

作者： Rajput, Kamran Yaseen Xiaoping, Li Lakhan, Abdullah School of Computer Science and Engineering Southeast University Nanjing China School of Economics Innovations and Technology Kristiania University College Oslo Norway Department of Cybersecurity Dawood University of Engineering and Technology Karachi Pakistan

The increasing adoption of hybrid clouds in organizations stems from their ability to bolster private cloud resources with additional public cloud capacity when required. However, scheduling distributed applications, such as workflow tasks, on hybrid cloud resources presents new and intricate challenges. A significant concern revolves around the potential exposure of private data and tasks within third-party, public cloud infrastructures, especially within sensitive domains like healthcare applications. The complexity escalates when considering the selection of resources from multiple cloud providers due to the fluctuating resource computation prices and data transmission costs. This paper presents the Spark Workflow Task Scheduling to Hybrid Cloud (SWSHC) framework, designed to schedule Spark workflows precisely while adhering to deadline and task privacy constraints within a hybrid cloud setting. Our innovative approach encompasses developing and implementing three pivotal components: deadline division, stage order optimization, and task scheduling mechanisms. We segregate the workflow deadline for each stage to bridge the gaps between stages effectively. Additionally, job prioritization is achieved using the maximum rank rule. The proposed solution considers diverse factors, including interval pricing variations, utilization of heterogeneous VM instances, intra- and inter-bandwidth considerations, and the efficient utilization of private cloud resources. Through meticulous calibration of our algorithm and comprehensive experimentation with various realistic workflows, our findings unequivocally demonstrate that SWSHC surpasses existing solutions in the current literature the cost by up to 40–70% in terms of cost efficiency and resource utilization. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Differential privacy

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Analysis of Blockchain Security Applications in Electronic Health Records Standardization

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Recent Advances in computer science and Communications 2023年第5期16卷 19-30页

作者： Mishra, Princy Khandelwal, Brijesh Dewangan, Bhupesh Kumar Amity School of Engineering Amity University Raipur India Department of Computer Science and Engineering School of Engineering O.P. Jindal University Raigarh India

Healthcare data is most unorganized and decentralized in many countries, including India. EHR (Electronic Health Record) has increased its acceptability and importance as it assists in medical research and helps backtrack the origin of disease. EHR may be generated at multiple sources like hospitals, clinics, path labs, or pharmacies. This paper is mainly targeted towards understanding the feasibility and different processes that could be adopted for medical data security;for this purpose, we have performed a systematic literature review. This research explores existing EHR frameworks, their benefits and challenges, as well as a brief comparison and applicability of various blockchain *** part of the review, we have discussed prior works, the background of blockchain in the healthcare domain, and bibliometric analysis on ample research papers from 2011 to 2021. © 2023 Bentham science Publishers.

关键词： Smart contract

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