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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Vijayalakshmi, V. Selvakumar, A. Panimalar, K. Cmr Institute of Technology Department of Information Science and Engineering Bengaluru India Simats School of Engineering Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9781665474672

Medical expenses are increasing day by day due to unexpected epidemic diseases. People have awareness about the medical insurance and claiming processes which is helping in critical situations. Predicting the medical insurance cost is very important work in health care sectors. Many researcheis applied different machine learning algoiithms to predict the insurance premium in Kaggle data set with seven attributes such as age, sex, bmi, children, smoker, region and charges. But these features only not suitable for prediction. In this paper, the dataset with 24 features including all relevant attributes needed for prediction of insurance cost was used. The implementation done using regression algorithms such as Linear Regression, Decision Tree Regression, Lasso Regression, Ridge Regression, Random Forest Regression, ElasticNet Regression, Support Vector Regression, K Nearest Neighbor Regression and Neural Network Regression in R Programming. There were seven metiices applied to measure the performance of the model namely Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), RSquared (R2), Adjusted R-Squared (Adj. R2), and Explained Variance Score (EVS). Random Forest Regression outperformed with 0.9533 as RS quared value. This prediction system will reduce the manual work and give the prediction accurately in medical field. © 2023 IEEE.

关键词： Forecasting

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Time series prediction and application based on multi-kernel support vector regression 2

Time series prediction and application based on multi-kernel...

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2nd International Symposium on Computer Applications and Information Systems, ISCAIS 2023

作者： Yi, Xiaoxing Wen, Xueting Yin, Xiangfeng School of Mathematics and Computational Science Hunan University of Science and Technology Hunan province Xiangtan China School of Mathematics and Computational Science Hunan University of Science and Technology Hunan Engineering Research Center for Big Data and Intelligent Decision-making of Industrial Development Hunan province Xiangtan China

ISBN: (纸本)9781510666665

In this paper, we propose a new MRMRKA-SVR-GARCH model to predict the volatility of time series. This model is a combination of the MRMRKA-SVR model and the GARCH model. Since SVR-GARCH model has been successfully employed to forecast volatility. Empirical results show that the multi-kernel support vector regression based on minimal redundancy maximal correlation criteria and kernel target alignment (MRMRKA-SVR) tend to beat single-kernel SVR models in terms of forecasting accuracy. First,we use the MRMRKA algorithm to select a set of kernels to establish a multi-kernel, and establish the MRMRKA-SVR model. Then we use MRMRKA-SVR to estimate the volatility equation of GARCH model, and establish MRMRKA-SVR-GARCH model. Furthermore, the models’ predictive ability was evaluated using three empirical analysises on China Unicom’s stock price data and stock price data of ICBC. Under different basic kermel numbers m, compare the prediction errors between the proposed model and the SVR-GARCH model based on the mixed Gaussian kernels in the two stock data, the results show that the prediction accuracy of the MRMRKA-SVR-GARCH model fluctuates slightly with the change of m, and the MAE and RMSE of the MRMRKASVR- GARCH model are lower than the mixed-Gaussian-kernel SVR-GARCH model . Description MRMRKA-SVRGARCH model managed to outperform the mixed-Gaussian-kernel SVR-GARCH model. Threrfore, the MRMRKASVR- GARCH model can provide a certain degree of reference in analyzing the trend and prediction of volatility, and has high feasibility and effectiveness. © 2023 SPIE.

关键词： Time series

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Analysis and Classification of Network Security Attacks Using Domain Generation Algorithm

Analysis and Classification of Network Security Attacks Usin...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Gomathi, S. Samundeeswari, S. Ponmanirai, S. Nanthini, S. Vandayar Engineering College Department of Computer Science and Engineering Thanjavur India Saveetha School of Engineering Simats University Department of Computational Intelligence Thandalam India Saveetha School of Engineering Simats University Department of Department of Data Science Thandalam India

ISBN: (纸本)9798350313987

In the realm of cybersecurity, the evolution of network attacks necessitates innovative approaches for threat prediction and mitigation. Traditional methods often fall short in identifying novel attack patterns or adapting swiftly to emerging threats. This study explores the application of Domain Generative Algorithms (DGAs) in the context of network attack prediction. DGAs leverage advanced machine learning techniques to generate synthetic data samples representing various network attack scenarios. By training generative models on diverse and large-scale datasets comprising both historical attack data and benign network traffic, the algorithms learn intricate patterns intrinsic to different types of attacks. Through, systematic analysis of these synthetic attack instances, IDS and IPS algorithms can be fine-tuned to recognize subtle attack patterns that might elude conventional signature-based detection methods. This research underscores the pivotal role of DGAs in revolutionizing the landscape of network attack prediction. By harnessing the power of generative algorithms, organizations can proactively strengthen their cybersecurity posture, effectively mitigating the risks posed by an ever-evolving spectrum of network attacks. © 2023 IEEE.

关键词： data Traffic Denial of Services Domain Generation Algorithm Malware Network security Pattern Analysis

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Know-Thy-Basis: Decomposing F26 for Lightweight S-box Implementation

IACR Transactions on Cryptographic Hardware and Embedded Sys...

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IACR Transactions on Cryptographic Hardware and Embedded Systems 2024年第4期2024卷 763-794页

作者： Sau, Dilip Sarkar, Sumanta Saha, Dhiman Mandal, Kalikinkar Center for Computational & Data Sciences Indian Institute of Technology Kharagpur India University of Warwick Coventry United Kingdom de.ci.phe.red Lab Department of Computer Science and Engineering Indian Institute of Technology Bhilai India University of New Brunswick FrederictonNB Canada

A recent trend has shown constructions of 6-bit S-boxes that are mostly focused on their cryptographic elegance, while their lightweight aspects have not really been addressed well. This paper attempts to plug-in this existing research gap where we show how the composite structure of the extension field F26 could be leveraged. An earlier well-known example is an efficient implementation of AES S-box using the tower field extension of F28. The case of F2ab is completely different from any tower field as the implementation varies as per the choice of extension – for instance, F(2a)b or F(2b)a, where a and b are prime. Thus, it makes the implementation of S-boxes over F26 = F2(2×3) very interesting. In this work, we systematically study the composite field structure of F26 from a hardware standpoint for a class of S-boxes that are power mapping or their affine equivalents. We analyze the hardware efficiency with respect to different representations of the field extension, i.e., F(22)3or F(23)2. Furthermore, for each extension, we investigate the impact of various choices of bases – for instance, we present the evidence of the effect that normal or polynomial bases have on the implementation. This gives us further insight on the choice of basis with respect to the field extension. In the process, we present a special normal basis, when used in conjunction with F(23)2 results in the least (or very close to least) area in terms of GE for the 18 (6 quadratic and 12 cubic) S-boxes studied in this work. The special normal basis reported here has some algebraic properties which make it inherently hardware friendly and allow us to predict the area reduction, without running a tool. Overall, this work constitutes an extensive hardware characterization of a class of cryptographically significant 6-bit S-boxes giving us interesting insights into the systematic lightweight implementation of S-boxes without relying on an automated tool. © 2024, Ruhr-University of Bochum. All rights

关键词： Cryptography

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Generalising Violence Detection with a New Near-Real-World Violence dataset 3

Generalising Violence Detection with a New Near-Real-World V...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Haque, Mahmudul Nyeem, Hussain Ovi, Tareque Bashar Nahid, Al Molla, Md. Sabbir Hossain Tuhin, Md. Tanjim Mahmud Shahab, Fardin Alam, Ayat Subah Alam, Saadia Binte Mirpur Cantonment Dhaka1216 Bangladesh Independent University Department of Computer Science and Engineering Dhaka1229 Bangladesh Independent University Bangladesh Center for Computational & Data Sciences Dhaka1229 Bangladesh

ISBN: (纸本)9798350388282

Detecting and classifying violence is crucial for public safety and addressing societal violence. DL models have greatly improved the automation of violence detection systems by effectively capturing intricate visual patterns. However, the quality and diversity of the training data greatly impact the effectiveness of these models. Existing datasets may be biased towards specific situations, limiting their practical use. To address this limitation, we introduce the Movie Clip (MC) dataset to enhance the generalisability of Automated Violence Detection and Classification (AVDC) systems. The MC dataset encompasses a broad spectrum of near-real-world violent actions, incorporating diverse demographics, environmental circumstances, and cultural elements extracted from movies. Consequently, it accurately reflects the complexity and diversity of real-world violent scenarios. The potential of the new dataset is investigated against its existing counterparts, like Hockey Fight (HF) and AIRTLab datasets. These datasets are used to train the ConvLSTM models based on the VGG16 and VGG19 architectures. The proposed dataset significantly improves AVDC model generalisation, outperforming the generalisability of current datasets, thereby advancing violence detection and facilitating the development of more robust and efficient AVDC systems. © 2024 IEEE.

关键词： Long short-term memory

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Optimal Design of Color Laparoscopic Super-Resolution Image Quality Based on Generative Adversarial Networks 1

Optimal Design of Color Laparoscopic Super-Resolution Image ...

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1st International Conference on Computer Graphics and Image Processing, CGIP 2023

作者： Kawabata, Norifumi Nakaguchi, Toshiya Hokkaido University Education and Research Center for Mathematical and Data Science Sapporo Japan Computational Imaging Lab Research Division of Computational Imaging Sapporo Japan Chiba University Center for Frontier Medical Engineering Chiba Japan

ISBN: (纸本)9798350333183

The Generative Adversarial Networks (GAN) is unsupervised learning enabled to transform according to data characteristics, though this generate unreal data by learning characteristics from data. As past our study, we discussed from the viewpoint of image quality for super-resolution of color laparoscopic image including SRCNN (Super-Resolution Convolutional Neural Network). However, it was not enough to compare to other neural network methods in our discussion. We consider that it is possible to support the medical image diagnosis by measuring whether the difference of both neural network method and image contents is affected or not for image quality. In this paper, first we carried out the objective image quality assessment by designing optimally of color laparoscopic super-resolution image using Generative Adversarial Networks (GAN). And then, we discussed for performance between methods comparing to result of SRCNN. On the other hand, from a view of information science, we consider that we need to verify experimentally for affect between network learning effect and generated image, and then to improve method. Therefore, we also discussed relationship between image quality and learning effect in color laparoscopic image generation using SRGAN. © 2023 IEEE.

关键词： Generative adversarial networks

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A genetic engineering algorithm for the generalized quadratic assignment problem

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Neural Computing and Applications 2025年第18期37卷 12253-12279页

作者： Sohrabi, Majid Fathollahi-Fard, Amir M. Gromov, Vasilii A. Dulebenets, Maxim A. School of Data Analysis and Artificial Intelligence National Research University Higher School of Economics 11 Pokrovsky Boulevard Moscow109028 Russia Laboratory for Models and Methods of Computational Pragmatics National Research University Higher School of Economics Moscow Russia Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Taipa China College of Engineering 2035 E Paul Dirac Dr. Sliger Building Suite 275 TallahasseeFL32310 United States

The generalized quadratic assignment problem (GQAP) poses a significant challenge in optimization, known for its NP-hard complexity and wide-ranging applications in supply chain and manufacturing contexts. While various metaheuristic algorithms have addressed the GQAP, this paper introduces a novel modification of the genetic algorithm (GA), namely the genetic engineering algorithm (GEA). A traditional GA initializes the population of solutions randomly and refines these solutions iteratively through crossover and mutation operators. However, its reliance on randomness often leads to premature convergence, susceptibility to local optima, and suboptimal solutions at termination of the algorithmic run. To address these drawbacks, our research presents a modified GA framework inspired by genetic engineering principles. This framework introduces novel search mechanisms mimicking gene mining processes, targeting population enhancement through insertion, purification, and genetic material exchange from elite solutions. The GEA adds three search scenarios to the traditional GA framework to exhibit desired characteristics from chromosomes, enhancing solution quality and convergence speed. This GEA demonstrates a superior performance across various search scenarios against state-of-the-art algorithms and exact solutions for the benchmark instances of the GQAP. Additionally, this study conducts comprehensive sensitivity analyses on the search scenarios of the GEA. By employing gene mining strategies, the proposed GEA offers promising solutions for effectively addressing the NP-hard optimization challenges of the GQAP on large-scale datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Sensitivity analysis

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A Classifier-Ensemble-Based Surrogate-Assisted Evolutionary Algorithm for Distributed data-Driven Optimization

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IEEE Transactions on Evolutionary Computation 2025年第3期29卷 711-725页

作者： Guo, Xiao-Qi Wei, Feng-Feng Zhang, Jun Chen, Wei-Neng South China University of Technology School of Computer Science and Engineering Guangzhou510006 China South China University of Technology State Key Laboratory of Subtropical Building of Big Data and Computational Intelligence Guangzhou510006 China Hanyang University ERICA School of Electrical and Engineering Ansan15588 Korea Republic of Nankai University College of Artificial Intelligence Tianjin300071 China

Surrogate-assisted evolutionary algorithms (SAEAs) have achieved effective performance in solving complex data-driven optimization problems. In the Internet of Things environment, the data of many problems are collected and processed in distributed network nodes and cannot be transmitted. As each local node can only access and build surrogate models based on partial data, local models are usually not accurate and even conflicting. To address these challenges, this article proposes a classifier-ensemble-based surrogate-assisted evolutionary algorithm (CESAEA) with the following features. First, the local nodes in CESAEA train classifiers as surrogate models based on their own data to classify candidates into several levels according to their fitness quality. The classifiers are less sensitive to the partial and biased data than regression models in local nodes. Second, the central node in CESAEA ensembles the local surrogates to form a global classifier with a relaxation condition to guide the evolutionary optimizer to generate promising candidates. The relaxation condition helps to overcome the problem of local model inconsistency. Overall, CESAEA is composed of local classifier construction, global classifier ensemble, classifier-assisted evolutionary optimization and local regression-assisted selection. As only classifiers are allowed to transmit from local nodes to the central node, the mapping relationship between decision vector and objective is hidden and thus data privacy is protected. The experimental results on benchmark functions as well as distributed feature selection problems verify the effectiveness of CESAEA compared to several state-of-the-art approaches. © 1997-2012 IEEE.

关键词： Linear programming

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Graph Neural Networks for Optimizing Reliability in Vehicle-to-Infrastructure Communication in Autonomous Systems 9

Graph Neural Networks for Optimizing Reliability in Vehicle-...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Neelamegam, G. Revathy, J. Kannukkiniyal, M. Devendran, M. Akshya, J. Choudhry, Mani Deepak Vsb College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Christ the King Engineering College Department of Artificial Intelligence and Data Science Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India Hindustan institute of technology Department of Computer Science and Engineering Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350377972

High reliance on autonomous systems necessitates efficient and reliable data exchange through Vehicle-to-Infrastructure (V2I) communication to have appropriate and stable network performance in dynamic vehicular environments. With heuristic approaches, Dijkstra's algorithm, and Deep Q-Networks (DQN), several methods are proposed for optimizing latency reduction, packet delivery rate improvement, and power efficiency in the direction of V2I communications. However, these approaches suffer from important challenges: low adaptability of the network to real-time dynamics, suboptimal resource usage, and difficulties in scalability towards complex large-scale networks. In this scenario, we propose a framework that incorporates a GNN inside autonomous systems for V2I communication to optimize performance. The GNN will process real-time graph features, including signal strength, packet loss, and mobility speed, as it dynamically changes in adaptation to the evolution of network behaviour and other functions. This work shows the superiority of the proposed GNN model for the efficiency of experimentally conducted simulations compared to the current models;it achieved latency reduced by up to 28%, 5-10% PDR, and 20% energy efficiency across the various scenarios with urban, rural, highway, and suburban networks. The results validate robustness and scalability for practical implementations by the proposed approach in real-world autonomous systems. Further development is possible with this framework: for example, the use of multi-agent reinforcement learning techniques within cooperative decision-making and the enhancement through blockchain-based security mechanisms to ensure the integrity of the data. © 2024 IEEE.

关键词： Graph neural networks

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Emergency Care Patient Prediction using Electronic Health Records (EHR) data: An End-to-End Machine Learning Pipeline 10

Emergency Care Patient Prediction using Electronic Health Re...

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10th International Conference on Future Internet of Things and Cloud, FiCloud 2023

作者： Sumon, Maruful Abedin Kwemebe, Tor A. Melapu, Venkata K. Hasan, Md Mohiuddin Jackson State University Computational Data-Enabled Science and Engineering Jackson United States Jackson State University Department of Mathematics and Computer Science Jackson United States Jackson State University Computer Science and Engineering Jackson United States

ISBN: (纸本)9798350316353

Using electronic health records (EHR) data for predicting the condition of patients who are in need of emergency care is a promising application of machine learning. With the help of machine learning, complex problems of identification of patterns can be solved with ease and accuracy when proper machine learning model is implemented. This paper uses EHR data to build an end-to-end machine learning pipeline for predicting the outcome of emergency care patients. As a machine learning process, the pipeline includes data preprocessing, feature engineering, model selection, data set training and testing, evaluation, and deployment. By using EHR data in this pipeline, the model has been trained and tested on a large dataset of many patients information with different types of machine learning algorithms to improve and get the best prediction accuracy. This pipeline can aid in managing primary care patients efficiently and effectively with the implementation of best suitable machine learning algorithm at the time of urgency. By implementing the model on the EHR dataset, an accuracy of more than 75\% is obtained which indicates the efficiency of the machine learning process in the process of identifying patients who need the emergency care more than the general patients when taken to the primary care or emergency. © 2023 IEEE.

关键词： Forecasting

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